{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "一、创建我们的模型"
      ],
      "metadata": {
        "id": "iuydY74WTr8R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1、导入依赖项"
      ],
      "metadata": {
        "id": "gW-IWjNfM2wh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TensorFlow is an open source machine learning library\n",
        "import tensorflow as tf\n",
        "# NumPy is a math library\n",
        "import numpy as np\n",
        "# Matplotlib is a graphing library\n",
        "import matplotlib.pyplot as plt\n",
        "# math is Python's math library\n",
        "import math"
      ],
      "metadata": {
        "id": "lAb5cJ_lT41E"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2、生成数据"
      ],
      "metadata": {
        "id": "FC3x5OqDM-dB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll generate this many sample datapoints\n",
        "SAMPLES=1000\n",
        "# Set a \"seed\" value, so we get the same random numbers each time we run this\n",
        "# notebook. Any number can be used here.\n",
        "SEED = 1337\n",
        "np.random.seed(SEED)\n",
        "tf.random.set_seed(SEED)\n",
        "# Generate a uniformly distributed set of random numbers in the range from\n",
        "# 0 to 2π, which covers a complete sine wave oscillation\n",
        "x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)\n",
        "# Shuffle the values to guarantee they're not in order\n",
        "np.random.shuffle(x_values)\n",
        "# Calculate the corresponding sine values\n",
        "y_values = np.sin(x_values)\n",
        "# Plot our data. The 'b.' argument tells the library to print blue dots.\n",
        "plt.plot(x_values, y_values, 'b.')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "TBMNGMocUdcK",
        "outputId": "2c818deb-3f99-4217-df0c-a4c87941a15b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "向数据中添加一些随机噪声，让数据更接近现实。可以有效的学习并做出很好的预测。"
      ],
      "metadata": {
        "id": "WdJsHLqLNZCJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add a small random number to each y value\n",
        "y_values += 0.1 * np.random.randn(*y_values.shape)\n",
        "# Plot our data\n",
        "plt.plot(x_values, y_values, 'b.')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "KKaKZSr2WozA",
        "outputId": "f8492b44-212d-4dd5-c4a0-b6d9ce67c170"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3、拆分数据（60%训练模型、20%验证、20%测试）"
      ],
      "metadata": {
        "id": "XTLj1eoqOCIY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll use 60% of our data for training and 20% for testing. The remaining 20%\n",
        "# will be used for validation. Calculate the indices of each section.\n",
        "TRAIN_SPLIT = int(0.6 * SAMPLES)\n",
        "TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)\n",
        "# Use np.split to chop our data into three parts.\n",
        "# The second argument to np.split is an array of indices where the data will be\n",
        "# split. We provide two indices, so the data will be divided into three chunks.\n",
        "x_train, x_validate, x_test = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "y_train, y_validate, y_test = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "# Double check that our splits add up correctly\n",
        "assert (x_train.size + x_validate.size + x_test.size) == SAMPLES\n",
        "# Plot the data in each partition in different colors:\n",
        "plt.plot(x_train, y_train, 'b.', label=\"Train\")\n",
        "plt.plot(x_validate, y_validate, 'y.', label=\"Validate\")\n",
        "plt.plot(x_test, y_test, 'r.', label=\"Test\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "J_YYyRt_XyEn",
        "outputId": "0575bdd0-6ba6-4914-ff66-b1a376408a27"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4、定义基本模型（回归模型），事实上就是使用TensorFlow的高级API——Keras来创建深度学习网络。"
      ],
      "metadata": {
        "id": "Fh5LUXy4OddI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll use Keras to create a simple model architecture\n",
        "from tensorflow.keras import layers\n",
        "model_1 = tf.keras.Sequential()\n",
        "# First layer takes a scalar input and feeds it through 16 \"neurons.\" The\n",
        "# neurons decide whether to activate based on the 'relu' activation function.\n",
        "model_1.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n",
        "# Final layer is a single neuron, since we want to output a single value\n",
        "model_1.add(layers.Dense(1))\n",
        "# Compile the model using a standard optimizer and loss function for regression\n",
        "model_1.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
        "# Print a summary of the model's architecture\n",
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7nTjqmQYHd1",
        "outputId": "e73d5072-0a6d-4666-a6c7-96bfd157a48d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 16)                32        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 49 (196.00 Byte)\n",
            "Trainable params: 49 (196.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "二、训练我们的模型。\n",
        "1、训练度量指标"
      ],
      "metadata": {
        "id": "KBrG9HW3PdaX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "history_1 = model_1.fit(x_train, y_train, epochs=1000, batch_size=16,\n",
        " validation_data=(x_validate, y_validate))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwzbHH1cIiwN",
        "outputId": "50836c1b-3a3b-4a5b-a042-0aa0eea173fe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "38/38 [==============================] - 3s 27ms/step - loss: 0.7224 - mae: 0.7525 - val_loss: 0.5473 - val_mae: 0.6626\n",
            "Epoch 2/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.4586 - mae: 0.5964 - val_loss: 0.4543 - val_mae: 0.5862\n",
            "Epoch 3/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.4069 - mae: 0.5521 - val_loss: 0.4140 - val_mae: 0.5624\n",
            "Epoch 4/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.3730 - mae: 0.5299 - val_loss: 0.3782 - val_mae: 0.5330\n",
            "Epoch 5/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.3419 - mae: 0.5089 - val_loss: 0.3446 - val_mae: 0.5049\n",
            "Epoch 6/1000\n",
            "38/38 [==============================] - 0s 10ms/step - loss: 0.3118 - mae: 0.4858 - val_loss: 0.3177 - val_mae: 0.4820\n",
            "Epoch 7/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2869 - mae: 0.4656 - val_loss: 0.2952 - val_mae: 0.4681\n",
            "Epoch 8/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2672 - mae: 0.4527 - val_loss: 0.2715 - val_mae: 0.4471\n",
            "Epoch 9/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2486 - mae: 0.4373 - val_loss: 0.2534 - val_mae: 0.4353\n",
            "Epoch 10/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2320 - mae: 0.4238 - val_loss: 0.2397 - val_mae: 0.4246\n",
            "Epoch 11/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2200 - mae: 0.4141 - val_loss: 0.2245 - val_mae: 0.4130\n",
            "Epoch 12/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.2089 - mae: 0.4032 - val_loss: 0.2165 - val_mae: 0.4063\n",
            "Epoch 13/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.2022 - mae: 0.3977 - val_loss: 0.2074 - val_mae: 0.3980\n",
            "Epoch 14/1000\n",
            "38/38 [==============================] - 0s 12ms/step - loss: 0.1956 - mae: 0.3919 - val_loss: 0.2021 - val_mae: 0.3900\n",
            "Epoch 15/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.1914 - mae: 0.3858 - val_loss: 0.1962 - val_mae: 0.3847\n",
            "Epoch 16/1000\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.1880 - mae: 0.3820 - val_loss: 0.1942 - val_mae: 0.3828\n",
            "Epoch 17/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1858 - mae: 0.3790 - val_loss: 0.1907 - val_mae: 0.3780\n",
            "Epoch 18/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.1831 - mae: 0.3745 - val_loss: 0.1885 - val_mae: 0.3753\n",
            "Epoch 19/1000\n",
            "38/38 [==============================] - 1s 14ms/step - loss: 0.1812 - mae: 0.3718 - val_loss: 0.1877 - val_mae: 0.3747\n",
            "Epoch 20/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1800 - mae: 0.3695 - val_loss: 0.1862 - val_mae: 0.3732\n",
            "Epoch 21/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1789 - mae: 0.3684 - val_loss: 0.1831 - val_mae: 0.3671\n",
            "Epoch 22/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.1774 - mae: 0.3671 - val_loss: 0.1823 - val_mae: 0.3640\n",
            "Epoch 23/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1767 - mae: 0.3629 - val_loss: 0.1812 - val_mae: 0.3628\n",
            "Epoch 24/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1749 - mae: 0.3611 - val_loss: 0.1834 - val_mae: 0.3584\n",
            "Epoch 25/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.1754 - mae: 0.3594 - val_loss: 0.1790 - val_mae: 0.3609\n",
            "Epoch 26/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1745 - mae: 0.3584 - val_loss: 0.1783 - val_mae: 0.3605\n",
            "Epoch 27/1000\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.1723 - mae: 0.3554 - val_loss: 0.1775 - val_mae: 0.3595\n",
            "Epoch 28/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1719 - mae: 0.3548 - val_loss: 0.1765 - val_mae: 0.3575\n",
            "Epoch 29/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1722 - mae: 0.3539 - val_loss: 0.1769 - val_mae: 0.3585\n",
            "Epoch 30/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1703 - mae: 0.3516 - val_loss: 0.1756 - val_mae: 0.3563\n",
            "Epoch 31/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1703 - mae: 0.3511 - val_loss: 0.1741 - val_mae: 0.3534\n",
            "Epoch 32/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1694 - mae: 0.3505 - val_loss: 0.1732 - val_mae: 0.3503\n",
            "Epoch 33/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1690 - mae: 0.3486 - val_loss: 0.1729 - val_mae: 0.3513\n",
            "Epoch 34/1000\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.1685 - mae: 0.3481 - val_loss: 0.1724 - val_mae: 0.3474\n",
            "Epoch 35/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1674 - mae: 0.3452 - val_loss: 0.1746 - val_mae: 0.3536\n",
            "Epoch 36/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1663 - mae: 0.3441 - val_loss: 0.1715 - val_mae: 0.3450\n",
            "Epoch 37/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1667 - mae: 0.3433 - val_loss: 0.1712 - val_mae: 0.3440\n",
            "Epoch 38/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1660 - mae: 0.3422 - val_loss: 0.1701 - val_mae: 0.3442\n",
            "Epoch 39/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1665 - mae: 0.3427 - val_loss: 0.1698 - val_mae: 0.3432\n",
            "Epoch 40/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1654 - mae: 0.3406 - val_loss: 0.1703 - val_mae: 0.3472\n",
            "Epoch 41/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1647 - mae: 0.3401 - val_loss: 0.1687 - val_mae: 0.3422\n",
            "Epoch 42/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1645 - mae: 0.3391 - val_loss: 0.1699 - val_mae: 0.3464\n",
            "Epoch 43/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1646 - mae: 0.3398 - val_loss: 0.1680 - val_mae: 0.3416\n",
            "Epoch 44/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1636 - mae: 0.3382 - val_loss: 0.1689 - val_mae: 0.3446\n",
            "Epoch 45/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1624 - mae: 0.3379 - val_loss: 0.1678 - val_mae: 0.3387\n",
            "Epoch 46/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1636 - mae: 0.3362 - val_loss: 0.1675 - val_mae: 0.3384\n",
            "Epoch 47/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1632 - mae: 0.3373 - val_loss: 0.1675 - val_mae: 0.3376\n",
            "Epoch 48/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1626 - mae: 0.3347 - val_loss: 0.1668 - val_mae: 0.3373\n",
            "Epoch 49/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1622 - mae: 0.3338 - val_loss: 0.1663 - val_mae: 0.3380\n",
            "Epoch 50/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1640 - mae: 0.3363 - val_loss: 0.1667 - val_mae: 0.3406\n",
            "Epoch 51/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1623 - mae: 0.3370 - val_loss: 0.1677 - val_mae: 0.3350\n",
            "Epoch 52/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1622 - mae: 0.3344 - val_loss: 0.1667 - val_mae: 0.3405\n",
            "Epoch 53/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1604 - mae: 0.3357 - val_loss: 0.1672 - val_mae: 0.3342\n",
            "Epoch 54/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1631 - mae: 0.3349 - val_loss: 0.1666 - val_mae: 0.3400\n",
            "Epoch 55/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1615 - mae: 0.3334 - val_loss: 0.1654 - val_mae: 0.3354\n",
            "Epoch 56/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1619 - mae: 0.3336 - val_loss: 0.1655 - val_mae: 0.3383\n",
            "Epoch 57/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1614 - mae: 0.3333 - val_loss: 0.1649 - val_mae: 0.3367\n",
            "Epoch 58/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1612 - mae: 0.3322 - val_loss: 0.1661 - val_mae: 0.3390\n",
            "Epoch 59/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1626 - mae: 0.3337 - val_loss: 0.1647 - val_mae: 0.3362\n",
            "Epoch 60/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1613 - mae: 0.3330 - val_loss: 0.1658 - val_mae: 0.3384\n",
            "Epoch 61/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1614 - mae: 0.3320 - val_loss: 0.1644 - val_mae: 0.3353\n",
            "Epoch 62/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1602 - mae: 0.3328 - val_loss: 0.1668 - val_mae: 0.3305\n",
            "Epoch 63/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1607 - mae: 0.3324 - val_loss: 0.1645 - val_mae: 0.3318\n",
            "Epoch 64/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1617 - mae: 0.3303 - val_loss: 0.1648 - val_mae: 0.3361\n",
            "Epoch 65/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1609 - mae: 0.3314 - val_loss: 0.1639 - val_mae: 0.3335\n",
            "Epoch 66/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1610 - mae: 0.3304 - val_loss: 0.1639 - val_mae: 0.3318\n",
            "Epoch 67/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1618 - mae: 0.3304 - val_loss: 0.1637 - val_mae: 0.3331\n",
            "Epoch 68/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1621 - mae: 0.3316 - val_loss: 0.1636 - val_mae: 0.3332\n",
            "Epoch 69/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1606 - mae: 0.3298 - val_loss: 0.1637 - val_mae: 0.3313\n",
            "Epoch 70/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1619 - mae: 0.3310 - val_loss: 0.1634 - val_mae: 0.3324\n",
            "Epoch 71/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1599 - mae: 0.3285 - val_loss: 0.1701 - val_mae: 0.3405\n",
            "Epoch 72/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1604 - mae: 0.3281 - val_loss: 0.1635 - val_mae: 0.3332\n",
            "Epoch 73/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1614 - mae: 0.3297 - val_loss: 0.1656 - val_mae: 0.3363\n",
            "Epoch 74/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1603 - mae: 0.3297 - val_loss: 0.1633 - val_mae: 0.3308\n",
            "Epoch 75/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1610 - mae: 0.3305 - val_loss: 0.1632 - val_mae: 0.3320\n",
            "Epoch 76/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1602 - mae: 0.3295 - val_loss: 0.1661 - val_mae: 0.3367\n",
            "Epoch 77/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1607 - mae: 0.3301 - val_loss: 0.1656 - val_mae: 0.3362\n",
            "Epoch 78/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1603 - mae: 0.3304 - val_loss: 0.1641 - val_mae: 0.3285\n",
            "Epoch 79/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1606 - mae: 0.3293 - val_loss: 0.1634 - val_mae: 0.3293\n",
            "Epoch 80/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1613 - mae: 0.3296 - val_loss: 0.1630 - val_mae: 0.3313\n",
            "Epoch 81/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1598 - mae: 0.3274 - val_loss: 0.1628 - val_mae: 0.3307\n",
            "Epoch 82/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1608 - mae: 0.3296 - val_loss: 0.1631 - val_mae: 0.3290\n",
            "Epoch 83/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1597 - mae: 0.3267 - val_loss: 0.1650 - val_mae: 0.3347\n",
            "Epoch 84/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1595 - mae: 0.3286 - val_loss: 0.1633 - val_mae: 0.3285\n",
            "Epoch 85/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1602 - mae: 0.3275 - val_loss: 0.1638 - val_mae: 0.3331\n",
            "Epoch 86/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1604 - mae: 0.3278 - val_loss: 0.1659 - val_mae: 0.3353\n",
            "Epoch 87/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1607 - mae: 0.3281 - val_loss: 0.1626 - val_mae: 0.3304\n",
            "Epoch 88/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1598 - mae: 0.3275 - val_loss: 0.1635 - val_mae: 0.3323\n",
            "Epoch 89/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1599 - mae: 0.3272 - val_loss: 0.1636 - val_mae: 0.3269\n",
            "Epoch 90/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1604 - mae: 0.3276 - val_loss: 0.1626 - val_mae: 0.3305\n",
            "Epoch 91/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1590 - mae: 0.3266 - val_loss: 0.1628 - val_mae: 0.3311\n",
            "Epoch 92/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1599 - mae: 0.3279 - val_loss: 0.1627 - val_mae: 0.3309\n",
            "Epoch 93/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1608 - mae: 0.3273 - val_loss: 0.1644 - val_mae: 0.3332\n",
            "Epoch 94/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1589 - mae: 0.3287 - val_loss: 0.1627 - val_mae: 0.3271\n",
            "Epoch 95/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1605 - mae: 0.3270 - val_loss: 0.1636 - val_mae: 0.3322\n",
            "Epoch 96/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1604 - mae: 0.3273 - val_loss: 0.1628 - val_mae: 0.3308\n",
            "Epoch 97/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1593 - mae: 0.3269 - val_loss: 0.1635 - val_mae: 0.3258\n",
            "Epoch 98/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1605 - mae: 0.3268 - val_loss: 0.1622 - val_mae: 0.3282\n",
            "Epoch 99/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1582 - mae: 0.3255 - val_loss: 0.1624 - val_mae: 0.3269\n",
            "Epoch 100/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1592 - mae: 0.3266 - val_loss: 0.1653 - val_mae: 0.3337\n",
            "Epoch 101/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1603 - mae: 0.3285 - val_loss: 0.1641 - val_mae: 0.3323\n",
            "Epoch 102/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1608 - mae: 0.3275 - val_loss: 0.1655 - val_mae: 0.3335\n",
            "Epoch 103/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1591 - mae: 0.3268 - val_loss: 0.1622 - val_mae: 0.3262\n",
            "Epoch 104/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1602 - mae: 0.3262 - val_loss: 0.1622 - val_mae: 0.3291\n",
            "Epoch 105/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1595 - mae: 0.3254 - val_loss: 0.1623 - val_mae: 0.3295\n",
            "Epoch 106/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1598 - mae: 0.3261 - val_loss: 0.1629 - val_mae: 0.3303\n",
            "Epoch 107/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1591 - mae: 0.3264 - val_loss: 0.1624 - val_mae: 0.3255\n",
            "Epoch 108/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1588 - mae: 0.3246 - val_loss: 0.1632 - val_mae: 0.3308\n",
            "Epoch 109/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1591 - mae: 0.3251 - val_loss: 0.1684 - val_mae: 0.3356\n",
            "Epoch 110/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3232 - val_loss: 0.1631 - val_mae: 0.3238\n",
            "Epoch 111/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1582 - mae: 0.3226 - val_loss: 0.1635 - val_mae: 0.3305\n",
            "Epoch 112/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1587 - mae: 0.3226 - val_loss: 0.1642 - val_mae: 0.3312\n",
            "Epoch 113/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1590 - mae: 0.3251 - val_loss: 0.1632 - val_mae: 0.3234\n",
            "Epoch 114/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1589 - mae: 0.3231 - val_loss: 0.1626 - val_mae: 0.3240\n",
            "Epoch 115/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1598 - mae: 0.3255 - val_loss: 0.1617 - val_mae: 0.3274\n",
            "Epoch 116/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1594 - mae: 0.3252 - val_loss: 0.1636 - val_mae: 0.3233\n",
            "Epoch 117/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3228 - val_loss: 0.1616 - val_mae: 0.3262\n",
            "Epoch 118/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3261 - val_loss: 0.1663 - val_mae: 0.3224\n",
            "Epoch 119/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3222 - val_loss: 0.1618 - val_mae: 0.3279\n",
            "Epoch 120/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1589 - mae: 0.3242 - val_loss: 0.1616 - val_mae: 0.3274\n",
            "Epoch 121/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1589 - mae: 0.3248 - val_loss: 0.1637 - val_mae: 0.3228\n",
            "Epoch 122/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1595 - mae: 0.3241 - val_loss: 0.1629 - val_mae: 0.3297\n",
            "Epoch 123/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1583 - mae: 0.3236 - val_loss: 0.1619 - val_mae: 0.3244\n",
            "Epoch 124/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1578 - mae: 0.3227 - val_loss: 0.1656 - val_mae: 0.3322\n",
            "Epoch 125/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1590 - mae: 0.3242 - val_loss: 0.1632 - val_mae: 0.3228\n",
            "Epoch 126/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1569 - mae: 0.3212 - val_loss: 0.1616 - val_mae: 0.3242\n",
            "Epoch 127/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1594 - mae: 0.3247 - val_loss: 0.1613 - val_mae: 0.3258\n",
            "Epoch 128/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1591 - mae: 0.3240 - val_loss: 0.1632 - val_mae: 0.3297\n",
            "Epoch 129/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1580 - mae: 0.3238 - val_loss: 0.1617 - val_mae: 0.3272\n",
            "Epoch 130/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1589 - mae: 0.3238 - val_loss: 0.1616 - val_mae: 0.3271\n",
            "Epoch 131/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1586 - mae: 0.3240 - val_loss: 0.1631 - val_mae: 0.3296\n",
            "Epoch 132/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1582 - mae: 0.3235 - val_loss: 0.1617 - val_mae: 0.3274\n",
            "Epoch 133/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1585 - mae: 0.3237 - val_loss: 0.1617 - val_mae: 0.3235\n",
            "Epoch 134/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1595 - mae: 0.3233 - val_loss: 0.1622 - val_mae: 0.3281\n",
            "Epoch 135/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1581 - mae: 0.3242 - val_loss: 0.1611 - val_mae: 0.3249\n",
            "Epoch 136/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1586 - mae: 0.3235 - val_loss: 0.1619 - val_mae: 0.3277\n",
            "Epoch 137/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1593 - mae: 0.3242 - val_loss: 0.1621 - val_mae: 0.3279\n",
            "Epoch 138/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1574 - mae: 0.3222 - val_loss: 0.1620 - val_mae: 0.3225\n",
            "Epoch 139/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1578 - mae: 0.3218 - val_loss: 0.1630 - val_mae: 0.3290\n",
            "Epoch 140/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1581 - mae: 0.3242 - val_loss: 0.1651 - val_mae: 0.3212\n",
            "Epoch 141/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1584 - mae: 0.3225 - val_loss: 0.1628 - val_mae: 0.3216\n",
            "Epoch 142/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1563 - mae: 0.3210 - val_loss: 0.1656 - val_mae: 0.3205\n",
            "Epoch 143/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1591 - mae: 0.3225 - val_loss: 0.1628 - val_mae: 0.3286\n",
            "Epoch 144/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1584 - mae: 0.3219 - val_loss: 0.1611 - val_mae: 0.3233\n",
            "Epoch 145/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1583 - mae: 0.3214 - val_loss: 0.1610 - val_mae: 0.3233\n",
            "Epoch 146/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1579 - mae: 0.3220 - val_loss: 0.1613 - val_mae: 0.3261\n",
            "Epoch 147/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1582 - mae: 0.3232 - val_loss: 0.1614 - val_mae: 0.3222\n",
            "Epoch 148/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1588 - mae: 0.3229 - val_loss: 0.1611 - val_mae: 0.3228\n",
            "Epoch 149/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1575 - mae: 0.3214 - val_loss: 0.1647 - val_mae: 0.3300\n",
            "Epoch 150/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1582 - mae: 0.3240 - val_loss: 0.1616 - val_mae: 0.3215\n",
            "Epoch 151/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1583 - mae: 0.3215 - val_loss: 0.1619 - val_mae: 0.3269\n",
            "Epoch 152/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1580 - mae: 0.3215 - val_loss: 0.1612 - val_mae: 0.3259\n",
            "Epoch 153/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1574 - mae: 0.3211 - val_loss: 0.1611 - val_mae: 0.3258\n",
            "Epoch 154/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1586 - mae: 0.3241 - val_loss: 0.1610 - val_mae: 0.3228\n",
            "Epoch 155/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3229 - val_loss: 0.1611 - val_mae: 0.3257\n",
            "Epoch 156/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1586 - mae: 0.3223 - val_loss: 0.1610 - val_mae: 0.3254\n",
            "Epoch 157/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3223 - val_loss: 0.1608 - val_mae: 0.3248\n",
            "Epoch 158/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1581 - mae: 0.3217 - val_loss: 0.1635 - val_mae: 0.3286\n",
            "Epoch 159/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3208 - val_loss: 0.1613 - val_mae: 0.3215\n",
            "Epoch 160/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1575 - mae: 0.3207 - val_loss: 0.1620 - val_mae: 0.3269\n",
            "Epoch 161/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1590 - mae: 0.3227 - val_loss: 0.1608 - val_mae: 0.3247\n",
            "Epoch 162/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1578 - mae: 0.3224 - val_loss: 0.1676 - val_mae: 0.3321\n",
            "Epoch 163/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3201 - val_loss: 0.1696 - val_mae: 0.3338\n",
            "Epoch 164/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3219 - val_loss: 0.1605 - val_mae: 0.3234\n",
            "Epoch 165/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1586 - mae: 0.3216 - val_loss: 0.1605 - val_mae: 0.3235\n",
            "Epoch 166/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3217 - val_loss: 0.1606 - val_mae: 0.3224\n",
            "Epoch 167/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1579 - mae: 0.3206 - val_loss: 0.1611 - val_mae: 0.3253\n",
            "Epoch 168/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1585 - mae: 0.3219 - val_loss: 0.1614 - val_mae: 0.3257\n",
            "Epoch 169/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3212 - val_loss: 0.1644 - val_mae: 0.3289\n",
            "Epoch 170/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1586 - mae: 0.3223 - val_loss: 0.1604 - val_mae: 0.3225\n",
            "Epoch 171/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1571 - mae: 0.3200 - val_loss: 0.1688 - val_mae: 0.3327\n",
            "Epoch 172/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1584 - mae: 0.3214 - val_loss: 0.1607 - val_mae: 0.3243\n",
            "Epoch 173/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1585 - mae: 0.3220 - val_loss: 0.1607 - val_mae: 0.3211\n",
            "Epoch 174/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1578 - mae: 0.3219 - val_loss: 0.1609 - val_mae: 0.3205\n",
            "Epoch 175/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1577 - mae: 0.3209 - val_loss: 0.1604 - val_mae: 0.3219\n",
            "Epoch 176/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1578 - mae: 0.3210 - val_loss: 0.1605 - val_mae: 0.3214\n",
            "Epoch 177/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1571 - mae: 0.3190 - val_loss: 0.1604 - val_mae: 0.3220\n",
            "Epoch 178/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1577 - mae: 0.3201 - val_loss: 0.1609 - val_mae: 0.3245\n",
            "Epoch 179/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3204 - val_loss: 0.1629 - val_mae: 0.3270\n",
            "Epoch 180/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1580 - mae: 0.3206 - val_loss: 0.1628 - val_mae: 0.3269\n",
            "Epoch 181/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1580 - mae: 0.3215 - val_loss: 0.1637 - val_mae: 0.3278\n",
            "Epoch 182/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1580 - mae: 0.3213 - val_loss: 0.1608 - val_mae: 0.3244\n",
            "Epoch 183/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1573 - mae: 0.3203 - val_loss: 0.1621 - val_mae: 0.3262\n",
            "Epoch 184/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3205 - val_loss: 0.1616 - val_mae: 0.3194\n",
            "Epoch 185/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1573 - mae: 0.3204 - val_loss: 0.1626 - val_mae: 0.3265\n",
            "Epoch 186/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3206 - val_loss: 0.1632 - val_mae: 0.3183\n",
            "Epoch 187/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3180 - val_loss: 0.1617 - val_mae: 0.3184\n",
            "Epoch 188/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1576 - mae: 0.3195 - val_loss: 0.1608 - val_mae: 0.3237\n",
            "Epoch 189/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1569 - mae: 0.3187 - val_loss: 0.1603 - val_mae: 0.3228\n",
            "Epoch 190/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1570 - mae: 0.3196 - val_loss: 0.1609 - val_mae: 0.3188\n",
            "Epoch 191/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1568 - mae: 0.3157 - val_loss: 0.1610 - val_mae: 0.3239\n",
            "Epoch 192/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1577 - mae: 0.3199 - val_loss: 0.1600 - val_mae: 0.3206\n",
            "Epoch 193/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1575 - mae: 0.3200 - val_loss: 0.1599 - val_mae: 0.3205\n",
            "Epoch 194/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3189 - val_loss: 0.1607 - val_mae: 0.3188\n",
            "Epoch 195/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3176 - val_loss: 0.1626 - val_mae: 0.3257\n",
            "Epoch 196/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3193 - val_loss: 0.1606 - val_mae: 0.3190\n",
            "Epoch 197/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1577 - mae: 0.3194 - val_loss: 0.1599 - val_mae: 0.3208\n",
            "Epoch 198/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1570 - mae: 0.3182 - val_loss: 0.1602 - val_mae: 0.3198\n",
            "Epoch 199/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1577 - mae: 0.3190 - val_loss: 0.1606 - val_mae: 0.3233\n",
            "Epoch 200/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3190 - val_loss: 0.1631 - val_mae: 0.3172\n",
            "Epoch 201/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3177 - val_loss: 0.1605 - val_mae: 0.3230\n",
            "Epoch 202/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3185 - val_loss: 0.1604 - val_mae: 0.3190\n",
            "Epoch 203/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1572 - mae: 0.3199 - val_loss: 0.1599 - val_mae: 0.3215\n",
            "Epoch 204/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3170 - val_loss: 0.1658 - val_mae: 0.3283\n",
            "Epoch 205/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3166 - val_loss: 0.1597 - val_mae: 0.3202\n",
            "Epoch 206/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1579 - mae: 0.3191 - val_loss: 0.1597 - val_mae: 0.3206\n",
            "Epoch 207/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3167 - val_loss: 0.1601 - val_mae: 0.3219\n",
            "Epoch 208/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3190 - val_loss: 0.1602 - val_mae: 0.3220\n",
            "Epoch 209/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1574 - mae: 0.3174 - val_loss: 0.1630 - val_mae: 0.3256\n",
            "Epoch 210/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3187 - val_loss: 0.1612 - val_mae: 0.3237\n",
            "Epoch 211/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1569 - mae: 0.3189 - val_loss: 0.1607 - val_mae: 0.3230\n",
            "Epoch 212/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1570 - mae: 0.3179 - val_loss: 0.1600 - val_mae: 0.3215\n",
            "Epoch 213/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3177 - val_loss: 0.1609 - val_mae: 0.3230\n",
            "Epoch 214/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1573 - mae: 0.3184 - val_loss: 0.1597 - val_mae: 0.3208\n",
            "Epoch 215/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1570 - mae: 0.3182 - val_loss: 0.1604 - val_mae: 0.3177\n",
            "Epoch 216/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3186 - val_loss: 0.1601 - val_mae: 0.3176\n",
            "Epoch 217/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1569 - mae: 0.3174 - val_loss: 0.1596 - val_mae: 0.3200\n",
            "Epoch 218/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1571 - mae: 0.3180 - val_loss: 0.1609 - val_mae: 0.3231\n",
            "Epoch 219/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3179 - val_loss: 0.1601 - val_mae: 0.3178\n",
            "Epoch 220/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3175 - val_loss: 0.1600 - val_mae: 0.3179\n",
            "Epoch 221/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3154 - val_loss: 0.1596 - val_mae: 0.3204\n",
            "Epoch 222/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3169 - val_loss: 0.1616 - val_mae: 0.3237\n",
            "Epoch 223/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1572 - mae: 0.3186 - val_loss: 0.1595 - val_mae: 0.3194\n",
            "Epoch 224/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1571 - mae: 0.3176 - val_loss: 0.1596 - val_mae: 0.3203\n",
            "Epoch 225/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1569 - mae: 0.3172 - val_loss: 0.1599 - val_mae: 0.3208\n",
            "Epoch 226/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3171 - val_loss: 0.1623 - val_mae: 0.3242\n",
            "Epoch 227/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1576 - mae: 0.3173 - val_loss: 0.1596 - val_mae: 0.3201\n",
            "Epoch 228/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3174 - val_loss: 0.1598 - val_mae: 0.3208\n",
            "Epoch 229/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3166 - val_loss: 0.1632 - val_mae: 0.3249\n",
            "Epoch 230/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1581 - mae: 0.3178 - val_loss: 0.1594 - val_mae: 0.3184\n",
            "Epoch 231/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1565 - mae: 0.3180 - val_loss: 0.1600 - val_mae: 0.3168\n",
            "Epoch 232/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1557 - mae: 0.3163 - val_loss: 0.1605 - val_mae: 0.3217\n",
            "Epoch 233/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1576 - mae: 0.3195 - val_loss: 0.1594 - val_mae: 0.3192\n",
            "Epoch 234/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1565 - mae: 0.3167 - val_loss: 0.1593 - val_mae: 0.3186\n",
            "Epoch 235/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1567 - mae: 0.3175 - val_loss: 0.1629 - val_mae: 0.3242\n",
            "Epoch 236/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1564 - mae: 0.3180 - val_loss: 0.1602 - val_mae: 0.3162\n",
            "Epoch 237/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1561 - mae: 0.3150 - val_loss: 0.1595 - val_mae: 0.3199\n",
            "Epoch 238/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1569 - mae: 0.3182 - val_loss: 0.1600 - val_mae: 0.3210\n",
            "Epoch 239/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1576 - mae: 0.3177 - val_loss: 0.1593 - val_mae: 0.3192\n",
            "Epoch 240/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1569 - mae: 0.3155 - val_loss: 0.1630 - val_mae: 0.3243\n",
            "Epoch 241/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1565 - mae: 0.3184 - val_loss: 0.1626 - val_mae: 0.3149\n",
            "Epoch 242/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1572 - mae: 0.3171 - val_loss: 0.1596 - val_mae: 0.3198\n",
            "Epoch 243/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3145 - val_loss: 0.1695 - val_mae: 0.3298\n",
            "Epoch 244/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1574 - mae: 0.3194 - val_loss: 0.1610 - val_mae: 0.3150\n",
            "Epoch 245/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1569 - mae: 0.3160 - val_loss: 0.1619 - val_mae: 0.3230\n",
            "Epoch 246/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1559 - mae: 0.3160 - val_loss: 0.1648 - val_mae: 0.3256\n",
            "Epoch 247/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1565 - mae: 0.3161 - val_loss: 0.1614 - val_mae: 0.3225\n",
            "Epoch 248/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1567 - mae: 0.3171 - val_loss: 0.1597 - val_mae: 0.3166\n",
            "Epoch 249/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1563 - mae: 0.3171 - val_loss: 0.1600 - val_mae: 0.3156\n",
            "Epoch 250/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1579 - mae: 0.3179 - val_loss: 0.1591 - val_mae: 0.3179\n",
            "Epoch 251/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1566 - mae: 0.3161 - val_loss: 0.1596 - val_mae: 0.3197\n",
            "Epoch 252/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1566 - mae: 0.3159 - val_loss: 0.1593 - val_mae: 0.3189\n",
            "Epoch 253/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1562 - mae: 0.3143 - val_loss: 0.1594 - val_mae: 0.3164\n",
            "Epoch 254/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1572 - mae: 0.3166 - val_loss: 0.1593 - val_mae: 0.3191\n",
            "Epoch 255/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1563 - mae: 0.3170 - val_loss: 0.1591 - val_mae: 0.3183\n",
            "Epoch 256/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3150 - val_loss: 0.1599 - val_mae: 0.3202\n",
            "Epoch 257/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3176 - val_loss: 0.1593 - val_mae: 0.3160\n",
            "Epoch 258/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1570 - mae: 0.3153 - val_loss: 0.1601 - val_mae: 0.3204\n",
            "Epoch 259/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3158 - val_loss: 0.1592 - val_mae: 0.3187\n",
            "Epoch 260/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3153 - val_loss: 0.1608 - val_mae: 0.3214\n",
            "Epoch 261/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1571 - mae: 0.3176 - val_loss: 0.1602 - val_mae: 0.3205\n",
            "Epoch 262/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3160 - val_loss: 0.1593 - val_mae: 0.3161\n",
            "Epoch 263/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3149 - val_loss: 0.1592 - val_mae: 0.3187\n",
            "Epoch 264/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3165 - val_loss: 0.1591 - val_mae: 0.3181\n",
            "Epoch 265/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1568 - mae: 0.3152 - val_loss: 0.1590 - val_mae: 0.3176\n",
            "Epoch 266/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3163 - val_loss: 0.1590 - val_mae: 0.3156\n",
            "Epoch 267/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1570 - mae: 0.3155 - val_loss: 0.1594 - val_mae: 0.3146\n",
            "Epoch 268/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1562 - mae: 0.3124 - val_loss: 0.1628 - val_mae: 0.3225\n",
            "Epoch 269/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3137 - val_loss: 0.1589 - val_mae: 0.3154\n",
            "Epoch 270/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3160 - val_loss: 0.1598 - val_mae: 0.3142\n",
            "Epoch 271/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3149 - val_loss: 0.1611 - val_mae: 0.3212\n",
            "Epoch 272/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3152 - val_loss: 0.1609 - val_mae: 0.3210\n",
            "Epoch 273/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3141 - val_loss: 0.1628 - val_mae: 0.3132\n",
            "Epoch 274/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3148 - val_loss: 0.1606 - val_mae: 0.3141\n",
            "Epoch 275/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3151 - val_loss: 0.1611 - val_mae: 0.3214\n",
            "Epoch 276/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3159 - val_loss: 0.1589 - val_mae: 0.3156\n",
            "Epoch 277/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.3150 - val_loss: 0.1599 - val_mae: 0.3138\n",
            "Epoch 278/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1562 - mae: 0.3151 - val_loss: 0.1609 - val_mae: 0.3208\n",
            "Epoch 279/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1568 - mae: 0.3140 - val_loss: 0.1587 - val_mae: 0.3162\n",
            "Epoch 280/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3144 - val_loss: 0.1610 - val_mae: 0.3208\n",
            "Epoch 281/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3154 - val_loss: 0.1588 - val_mae: 0.3161\n",
            "Epoch 282/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3160 - val_loss: 0.1588 - val_mae: 0.3154\n",
            "Epoch 283/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3165 - val_loss: 0.1589 - val_mae: 0.3173\n",
            "Epoch 284/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3150 - val_loss: 0.1601 - val_mae: 0.3136\n",
            "Epoch 285/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3152 - val_loss: 0.1599 - val_mae: 0.3139\n",
            "Epoch 286/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3126 - val_loss: 0.1612 - val_mae: 0.3211\n",
            "Epoch 287/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1562 - mae: 0.3135 - val_loss: 0.1591 - val_mae: 0.3178\n",
            "Epoch 288/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3138 - val_loss: 0.1686 - val_mae: 0.3273\n",
            "Epoch 289/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1568 - mae: 0.3149 - val_loss: 0.1588 - val_mae: 0.3170\n",
            "Epoch 290/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1552 - mae: 0.3133 - val_loss: 0.1587 - val_mae: 0.3165\n",
            "Epoch 291/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1565 - mae: 0.3159 - val_loss: 0.1588 - val_mae: 0.3171\n",
            "Epoch 292/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3138 - val_loss: 0.1615 - val_mae: 0.3129\n",
            "Epoch 293/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3144 - val_loss: 0.1591 - val_mae: 0.3145\n",
            "Epoch 294/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3149 - val_loss: 0.1634 - val_mae: 0.3117\n",
            "Epoch 295/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1567 - mae: 0.3159 - val_loss: 0.1587 - val_mae: 0.3144\n",
            "Epoch 296/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1561 - mae: 0.3144 - val_loss: 0.1587 - val_mae: 0.3165\n",
            "Epoch 297/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3144 - val_loss: 0.1597 - val_mae: 0.3187\n",
            "Epoch 298/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3140 - val_loss: 0.1592 - val_mae: 0.3137\n",
            "Epoch 299/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3141 - val_loss: 0.1609 - val_mae: 0.3126\n",
            "Epoch 300/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3115 - val_loss: 0.1597 - val_mae: 0.3188\n",
            "Epoch 301/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3139 - val_loss: 0.1586 - val_mae: 0.3156\n",
            "Epoch 302/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3149 - val_loss: 0.1591 - val_mae: 0.3139\n",
            "Epoch 303/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3137 - val_loss: 0.1590 - val_mae: 0.3137\n",
            "Epoch 304/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3146 - val_loss: 0.1586 - val_mae: 0.3154\n",
            "Epoch 305/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1562 - mae: 0.3150 - val_loss: 0.1593 - val_mae: 0.3135\n",
            "Epoch 306/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3141 - val_loss: 0.1592 - val_mae: 0.3137\n",
            "Epoch 307/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1567 - mae: 0.3141 - val_loss: 0.1588 - val_mae: 0.3170\n",
            "Epoch 308/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3135 - val_loss: 0.1618 - val_mae: 0.3213\n",
            "Epoch 309/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3124 - val_loss: 0.1611 - val_mae: 0.3204\n",
            "Epoch 310/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3143 - val_loss: 0.1617 - val_mae: 0.3123\n",
            "Epoch 311/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3149 - val_loss: 0.1611 - val_mae: 0.3203\n",
            "Epoch 312/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3143 - val_loss: 0.1607 - val_mae: 0.3199\n",
            "Epoch 313/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1564 - mae: 0.3146 - val_loss: 0.1587 - val_mae: 0.3166\n",
            "Epoch 314/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3132 - val_loss: 0.1586 - val_mae: 0.3160\n",
            "Epoch 315/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3147 - val_loss: 0.1600 - val_mae: 0.3191\n",
            "Epoch 316/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3142 - val_loss: 0.1590 - val_mae: 0.3138\n",
            "Epoch 317/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3132 - val_loss: 0.1625 - val_mae: 0.3118\n",
            "Epoch 318/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1571 - mae: 0.3136 - val_loss: 0.1613 - val_mae: 0.3202\n",
            "Epoch 319/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3134 - val_loss: 0.1591 - val_mae: 0.3131\n",
            "Epoch 320/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3130 - val_loss: 0.1593 - val_mae: 0.3178\n",
            "Epoch 321/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3124 - val_loss: 0.1587 - val_mae: 0.3167\n",
            "Epoch 322/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.3135 - val_loss: 0.1592 - val_mae: 0.3133\n",
            "Epoch 323/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3149 - val_loss: 0.1601 - val_mae: 0.3191\n",
            "Epoch 324/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3154 - val_loss: 0.1610 - val_mae: 0.3122\n",
            "Epoch 325/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1569 - mae: 0.3132 - val_loss: 0.1593 - val_mae: 0.3179\n",
            "Epoch 326/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3141 - val_loss: 0.1592 - val_mae: 0.3132\n",
            "Epoch 327/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3133 - val_loss: 0.1621 - val_mae: 0.3121\n",
            "Epoch 328/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1559 - mae: 0.3148 - val_loss: 0.1588 - val_mae: 0.3143\n",
            "Epoch 329/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3145 - val_loss: 0.1585 - val_mae: 0.3152\n",
            "Epoch 330/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1546 - mae: 0.3140 - val_loss: 0.1602 - val_mae: 0.3125\n",
            "Epoch 331/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1556 - mae: 0.3138 - val_loss: 0.1589 - val_mae: 0.3138\n",
            "Epoch 332/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1543 - mae: 0.3113 - val_loss: 0.1586 - val_mae: 0.3150\n",
            "Epoch 333/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1563 - mae: 0.3137 - val_loss: 0.1599 - val_mae: 0.3187\n",
            "Epoch 334/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3134 - val_loss: 0.1641 - val_mae: 0.3227\n",
            "Epoch 335/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1544 - mae: 0.3123 - val_loss: 0.1598 - val_mae: 0.3130\n",
            "Epoch 336/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1549 - mae: 0.3128 - val_loss: 0.1618 - val_mae: 0.3116\n",
            "Epoch 337/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1551 - mae: 0.3140 - val_loss: 0.1610 - val_mae: 0.3121\n",
            "Epoch 338/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1565 - mae: 0.3138 - val_loss: 0.1617 - val_mae: 0.3205\n",
            "Epoch 339/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1557 - mae: 0.3146 - val_loss: 0.1588 - val_mae: 0.3167\n",
            "Epoch 340/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1532 - mae: 0.3112 - val_loss: 0.1733 - val_mae: 0.3297\n",
            "Epoch 341/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1560 - mae: 0.3148 - val_loss: 0.1618 - val_mae: 0.3117\n",
            "Epoch 342/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1559 - mae: 0.3129 - val_loss: 0.1585 - val_mae: 0.3139\n",
            "Epoch 343/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1552 - mae: 0.3128 - val_loss: 0.1584 - val_mae: 0.3141\n",
            "Epoch 344/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1554 - mae: 0.3122 - val_loss: 0.1587 - val_mae: 0.3130\n",
            "Epoch 345/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1551 - mae: 0.3138 - val_loss: 0.1600 - val_mae: 0.3120\n",
            "Epoch 346/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1554 - mae: 0.3121 - val_loss: 0.1635 - val_mae: 0.3218\n",
            "Epoch 347/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1565 - mae: 0.3147 - val_loss: 0.1583 - val_mae: 0.3147\n",
            "Epoch 348/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1544 - mae: 0.3124 - val_loss: 0.1584 - val_mae: 0.3153\n",
            "Epoch 349/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1554 - mae: 0.3144 - val_loss: 0.1584 - val_mae: 0.3141\n",
            "Epoch 350/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1544 - mae: 0.3108 - val_loss: 0.1585 - val_mae: 0.3135\n",
            "Epoch 351/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3144 - val_loss: 0.1606 - val_mae: 0.3192\n",
            "Epoch 352/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1572 - mae: 0.3147 - val_loss: 0.1586 - val_mae: 0.3162\n",
            "Epoch 353/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1553 - mae: 0.3123 - val_loss: 0.1603 - val_mae: 0.3189\n",
            "Epoch 354/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1558 - mae: 0.3140 - val_loss: 0.1584 - val_mae: 0.3145\n",
            "Epoch 355/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3127 - val_loss: 0.1584 - val_mae: 0.3154\n",
            "Epoch 356/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3123 - val_loss: 0.1643 - val_mae: 0.3225\n",
            "Epoch 357/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3129 - val_loss: 0.1585 - val_mae: 0.3133\n",
            "Epoch 358/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3110 - val_loss: 0.1694 - val_mae: 0.3265\n",
            "Epoch 359/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3118 - val_loss: 0.1586 - val_mae: 0.3161\n",
            "Epoch 360/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3123 - val_loss: 0.1608 - val_mae: 0.3116\n",
            "Epoch 361/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3125 - val_loss: 0.1585 - val_mae: 0.3129\n",
            "Epoch 362/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3122 - val_loss: 0.1599 - val_mae: 0.3180\n",
            "Epoch 363/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3128 - val_loss: 0.1633 - val_mae: 0.3213\n",
            "Epoch 364/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1553 - mae: 0.3119 - val_loss: 0.1614 - val_mae: 0.3196\n",
            "Epoch 365/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3120 - val_loss: 0.1588 - val_mae: 0.3165\n",
            "Epoch 366/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3132 - val_loss: 0.1592 - val_mae: 0.3172\n",
            "Epoch 367/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3135 - val_loss: 0.1584 - val_mae: 0.3134\n",
            "Epoch 368/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3121 - val_loss: 0.1601 - val_mae: 0.3183\n",
            "Epoch 369/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3130 - val_loss: 0.1591 - val_mae: 0.3167\n",
            "Epoch 370/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3122 - val_loss: 0.1603 - val_mae: 0.3183\n",
            "Epoch 371/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3127 - val_loss: 0.1586 - val_mae: 0.3160\n",
            "Epoch 372/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3125 - val_loss: 0.1582 - val_mae: 0.3137\n",
            "Epoch 373/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3116 - val_loss: 0.1582 - val_mae: 0.3141\n",
            "Epoch 374/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3131 - val_loss: 0.1583 - val_mae: 0.3138\n",
            "Epoch 375/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3128 - val_loss: 0.1621 - val_mae: 0.3199\n",
            "Epoch 376/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3116 - val_loss: 0.1582 - val_mae: 0.3146\n",
            "Epoch 377/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3139 - val_loss: 0.1582 - val_mae: 0.3135\n",
            "Epoch 378/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3115 - val_loss: 0.1654 - val_mae: 0.3229\n",
            "Epoch 379/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3140 - val_loss: 0.1593 - val_mae: 0.3118\n",
            "Epoch 380/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3119 - val_loss: 0.1595 - val_mae: 0.3113\n",
            "Epoch 381/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3124 - val_loss: 0.1592 - val_mae: 0.3169\n",
            "Epoch 382/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3123 - val_loss: 0.1587 - val_mae: 0.3122\n",
            "Epoch 383/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3115 - val_loss: 0.1593 - val_mae: 0.3170\n",
            "Epoch 384/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3126 - val_loss: 0.1583 - val_mae: 0.3149\n",
            "Epoch 385/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3118 - val_loss: 0.1594 - val_mae: 0.3169\n",
            "Epoch 386/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3118 - val_loss: 0.1582 - val_mae: 0.3128\n",
            "Epoch 387/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3118 - val_loss: 0.1613 - val_mae: 0.3102\n",
            "Epoch 388/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3135 - val_loss: 0.1597 - val_mae: 0.3106\n",
            "Epoch 389/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3124 - val_loss: 0.1584 - val_mae: 0.3149\n",
            "Epoch 390/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3124 - val_loss: 0.1587 - val_mae: 0.3158\n",
            "Epoch 391/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3129 - val_loss: 0.1598 - val_mae: 0.3105\n",
            "Epoch 392/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3126 - val_loss: 0.1599 - val_mae: 0.3101\n",
            "Epoch 393/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3114 - val_loss: 0.1586 - val_mae: 0.3153\n",
            "Epoch 394/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1556 - mae: 0.3121 - val_loss: 0.1581 - val_mae: 0.3121\n",
            "Epoch 395/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1565 - mae: 0.3115 - val_loss: 0.1580 - val_mae: 0.3132\n",
            "Epoch 396/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3097 - val_loss: 0.1611 - val_mae: 0.3094\n",
            "Epoch 397/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3110 - val_loss: 0.1596 - val_mae: 0.3166\n",
            "Epoch 398/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3119 - val_loss: 0.1587 - val_mae: 0.3156\n",
            "Epoch 399/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3119 - val_loss: 0.1596 - val_mae: 0.3168\n",
            "Epoch 400/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3123 - val_loss: 0.1585 - val_mae: 0.3149\n",
            "Epoch 401/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3117 - val_loss: 0.1588 - val_mae: 0.3157\n",
            "Epoch 402/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3114 - val_loss: 0.1589 - val_mae: 0.3108\n",
            "Epoch 403/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3104 - val_loss: 0.1585 - val_mae: 0.3151\n",
            "Epoch 404/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3115 - val_loss: 0.1594 - val_mae: 0.3165\n",
            "Epoch 405/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3109 - val_loss: 0.1579 - val_mae: 0.3129\n",
            "Epoch 406/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3122 - val_loss: 0.1581 - val_mae: 0.3117\n",
            "Epoch 407/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3110 - val_loss: 0.1598 - val_mae: 0.3170\n",
            "Epoch 408/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3127 - val_loss: 0.1585 - val_mae: 0.3110\n",
            "Epoch 409/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3105 - val_loss: 0.1644 - val_mae: 0.3214\n",
            "Epoch 410/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3122 - val_loss: 0.1615 - val_mae: 0.3186\n",
            "Epoch 411/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1559 - mae: 0.3113 - val_loss: 0.1580 - val_mae: 0.3121\n",
            "Epoch 412/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3118 - val_loss: 0.1633 - val_mae: 0.3093\n",
            "Epoch 413/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3108 - val_loss: 0.1589 - val_mae: 0.3156\n",
            "Epoch 414/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3115 - val_loss: 0.1589 - val_mae: 0.3158\n",
            "Epoch 415/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3112 - val_loss: 0.1580 - val_mae: 0.3128\n",
            "Epoch 416/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3116 - val_loss: 0.1596 - val_mae: 0.3100\n",
            "Epoch 417/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3113 - val_loss: 0.1578 - val_mae: 0.3122\n",
            "Epoch 418/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3121 - val_loss: 0.1578 - val_mae: 0.3122\n",
            "Epoch 419/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3102 - val_loss: 0.1632 - val_mae: 0.3090\n",
            "Epoch 420/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3096 - val_loss: 0.1628 - val_mae: 0.3090\n",
            "Epoch 421/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1571 - mae: 0.3130 - val_loss: 0.1598 - val_mae: 0.3166\n",
            "Epoch 422/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3095 - val_loss: 0.1583 - val_mae: 0.3142\n",
            "Epoch 423/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3115 - val_loss: 0.1583 - val_mae: 0.3106\n",
            "Epoch 424/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.3118 - val_loss: 0.1583 - val_mae: 0.3142\n",
            "Epoch 425/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3118 - val_loss: 0.1585 - val_mae: 0.3145\n",
            "Epoch 426/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3108 - val_loss: 0.1580 - val_mae: 0.3134\n",
            "Epoch 427/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3119 - val_loss: 0.1591 - val_mae: 0.3156\n",
            "Epoch 428/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1535 - mae: 0.3090 - val_loss: 0.1629 - val_mae: 0.3195\n",
            "Epoch 429/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1556 - mae: 0.3116 - val_loss: 0.1580 - val_mae: 0.3134\n",
            "Epoch 430/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1542 - mae: 0.3101 - val_loss: 0.1598 - val_mae: 0.3165\n",
            "Epoch 431/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1554 - mae: 0.3112 - val_loss: 0.1588 - val_mae: 0.3102\n",
            "Epoch 432/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1552 - mae: 0.3114 - val_loss: 0.1608 - val_mae: 0.3178\n",
            "Epoch 433/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1552 - mae: 0.3121 - val_loss: 0.1585 - val_mae: 0.3147\n",
            "Epoch 434/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1529 - mae: 0.3106 - val_loss: 0.1614 - val_mae: 0.3092\n",
            "Epoch 435/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1554 - mae: 0.3111 - val_loss: 0.1581 - val_mae: 0.3110\n",
            "Epoch 436/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1559 - mae: 0.3122 - val_loss: 0.1579 - val_mae: 0.3129\n",
            "Epoch 437/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3103 - val_loss: 0.1585 - val_mae: 0.3146\n",
            "Epoch 438/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.3115 - val_loss: 0.1579 - val_mae: 0.3115\n",
            "Epoch 439/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1549 - mae: 0.3108 - val_loss: 0.1602 - val_mae: 0.3095\n",
            "Epoch 440/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.3117 - val_loss: 0.1584 - val_mae: 0.3104\n",
            "Epoch 441/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1555 - mae: 0.3101 - val_loss: 0.1592 - val_mae: 0.3157\n",
            "Epoch 442/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1547 - mae: 0.3115 - val_loss: 0.1592 - val_mae: 0.3158\n",
            "Epoch 443/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1546 - mae: 0.3119 - val_loss: 0.1617 - val_mae: 0.3090\n",
            "Epoch 444/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1537 - mae: 0.3106 - val_loss: 0.1633 - val_mae: 0.3202\n",
            "Epoch 445/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1554 - mae: 0.3115 - val_loss: 0.1613 - val_mae: 0.3183\n",
            "Epoch 446/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1559 - mae: 0.3114 - val_loss: 0.1582 - val_mae: 0.3112\n",
            "Epoch 447/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1545 - mae: 0.3095 - val_loss: 0.1598 - val_mae: 0.3098\n",
            "Epoch 448/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1550 - mae: 0.3104 - val_loss: 0.1590 - val_mae: 0.3155\n",
            "Epoch 449/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1552 - mae: 0.3123 - val_loss: 0.1617 - val_mae: 0.3184\n",
            "Epoch 450/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1547 - mae: 0.3121 - val_loss: 0.1582 - val_mae: 0.3142\n",
            "Epoch 451/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1553 - mae: 0.3129 - val_loss: 0.1585 - val_mae: 0.3148\n",
            "Epoch 452/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3122 - val_loss: 0.1580 - val_mae: 0.3135\n",
            "Epoch 453/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3108 - val_loss: 0.1591 - val_mae: 0.3110\n",
            "Epoch 454/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1529 - mae: 0.3086 - val_loss: 0.1703 - val_mae: 0.3101\n",
            "Epoch 455/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3104 - val_loss: 0.1581 - val_mae: 0.3141\n",
            "Epoch 456/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3115 - val_loss: 0.1580 - val_mae: 0.3115\n",
            "Epoch 457/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3121 - val_loss: 0.1581 - val_mae: 0.3139\n",
            "Epoch 458/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3104 - val_loss: 0.1578 - val_mae: 0.3128\n",
            "Epoch 459/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3106 - val_loss: 0.1579 - val_mae: 0.3118\n",
            "Epoch 460/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3110 - val_loss: 0.1589 - val_mae: 0.3154\n",
            "Epoch 461/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3107 - val_loss: 0.1604 - val_mae: 0.3172\n",
            "Epoch 462/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3108 - val_loss: 0.1589 - val_mae: 0.3154\n",
            "Epoch 463/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3120 - val_loss: 0.1585 - val_mae: 0.3100\n",
            "Epoch 464/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3114 - val_loss: 0.1582 - val_mae: 0.3139\n",
            "Epoch 465/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3091 - val_loss: 0.1579 - val_mae: 0.3131\n",
            "Epoch 466/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3101 - val_loss: 0.1582 - val_mae: 0.3101\n",
            "Epoch 467/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3088 - val_loss: 0.1577 - val_mae: 0.3117\n",
            "Epoch 468/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3110 - val_loss: 0.1585 - val_mae: 0.3095\n",
            "Epoch 469/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3085 - val_loss: 0.1589 - val_mae: 0.3150\n",
            "Epoch 470/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1553 - mae: 0.3124 - val_loss: 0.1578 - val_mae: 0.3124\n",
            "Epoch 471/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3102 - val_loss: 0.1577 - val_mae: 0.3120\n",
            "Epoch 472/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1560 - mae: 0.3117 - val_loss: 0.1589 - val_mae: 0.3094\n",
            "Epoch 473/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3088 - val_loss: 0.1606 - val_mae: 0.3172\n",
            "Epoch 474/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3120 - val_loss: 0.1578 - val_mae: 0.3121\n",
            "Epoch 475/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1551 - mae: 0.3120 - val_loss: 0.1582 - val_mae: 0.3104\n",
            "Epoch 476/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1533 - mae: 0.3088 - val_loss: 0.1584 - val_mae: 0.3145\n",
            "Epoch 477/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1539 - mae: 0.3119 - val_loss: 0.1651 - val_mae: 0.3215\n",
            "Epoch 478/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1557 - mae: 0.3122 - val_loss: 0.1625 - val_mae: 0.3189\n",
            "Epoch 479/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3115 - val_loss: 0.1635 - val_mae: 0.3201\n",
            "Epoch 480/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1526 - mae: 0.3089 - val_loss: 0.1579 - val_mae: 0.3126\n",
            "Epoch 481/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3100 - val_loss: 0.1582 - val_mae: 0.3140\n",
            "Epoch 482/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3095 - val_loss: 0.1584 - val_mae: 0.3098\n",
            "Epoch 483/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3105 - val_loss: 0.1579 - val_mae: 0.3130\n",
            "Epoch 484/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3099 - val_loss: 0.1585 - val_mae: 0.3143\n",
            "Epoch 485/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3110 - val_loss: 0.1590 - val_mae: 0.3095\n",
            "Epoch 486/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3090 - val_loss: 0.1578 - val_mae: 0.3126\n",
            "Epoch 487/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3100 - val_loss: 0.1579 - val_mae: 0.3107\n",
            "Epoch 488/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3098 - val_loss: 0.1578 - val_mae: 0.3124\n",
            "Epoch 489/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3104 - val_loss: 0.1578 - val_mae: 0.3129\n",
            "Epoch 490/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3117 - val_loss: 0.1670 - val_mae: 0.3231\n",
            "Epoch 491/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3123 - val_loss: 0.1581 - val_mae: 0.3106\n",
            "Epoch 492/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3104 - val_loss: 0.1604 - val_mae: 0.3170\n",
            "Epoch 493/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3103 - val_loss: 0.1586 - val_mae: 0.3145\n",
            "Epoch 494/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3115 - val_loss: 0.1624 - val_mae: 0.3083\n",
            "Epoch 495/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3100 - val_loss: 0.1577 - val_mae: 0.3123\n",
            "Epoch 496/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3110 - val_loss: 0.1577 - val_mae: 0.3118\n",
            "Epoch 497/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3095 - val_loss: 0.1615 - val_mae: 0.3176\n",
            "Epoch 498/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3104 - val_loss: 0.1596 - val_mae: 0.3157\n",
            "Epoch 499/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3088 - val_loss: 0.1583 - val_mae: 0.3138\n",
            "Epoch 500/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1548 - mae: 0.3106 - val_loss: 0.1580 - val_mae: 0.3132\n",
            "Epoch 501/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3104 - val_loss: 0.1606 - val_mae: 0.3169\n",
            "Epoch 502/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3092 - val_loss: 0.1606 - val_mae: 0.3169\n",
            "Epoch 503/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3099 - val_loss: 0.1578 - val_mae: 0.3125\n",
            "Epoch 504/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3091 - val_loss: 0.1610 - val_mae: 0.3078\n",
            "Epoch 505/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3099 - val_loss: 0.1581 - val_mae: 0.3095\n",
            "Epoch 506/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3097 - val_loss: 0.1581 - val_mae: 0.3132\n",
            "Epoch 507/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3107 - val_loss: 0.1576 - val_mae: 0.3113\n",
            "Epoch 508/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3081 - val_loss: 0.1592 - val_mae: 0.3152\n",
            "Epoch 509/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3108 - val_loss: 0.1577 - val_mae: 0.3120\n",
            "Epoch 510/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3104 - val_loss: 0.1576 - val_mae: 0.3104\n",
            "Epoch 511/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3102 - val_loss: 0.1600 - val_mae: 0.3081\n",
            "Epoch 512/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3071 - val_loss: 0.1689 - val_mae: 0.3235\n",
            "Epoch 513/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3095 - val_loss: 0.1578 - val_mae: 0.3099\n",
            "Epoch 514/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3104 - val_loss: 0.1580 - val_mae: 0.3093\n",
            "Epoch 515/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3105 - val_loss: 0.1575 - val_mae: 0.3109\n",
            "Epoch 516/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1531 - mae: 0.3088 - val_loss: 0.1588 - val_mae: 0.3083\n",
            "Epoch 517/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3075 - val_loss: 0.1586 - val_mae: 0.3139\n",
            "Epoch 518/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3102 - val_loss: 0.1583 - val_mae: 0.3135\n",
            "Epoch 519/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3103 - val_loss: 0.1582 - val_mae: 0.3134\n",
            "Epoch 520/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3108 - val_loss: 0.1576 - val_mae: 0.3109\n",
            "Epoch 521/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3102 - val_loss: 0.1579 - val_mae: 0.3099\n",
            "Epoch 522/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3092 - val_loss: 0.1636 - val_mae: 0.3193\n",
            "Epoch 523/1000\n",
            "38/38 [==============================] - 1s 22ms/step - loss: 0.1550 - mae: 0.3101 - val_loss: 0.1637 - val_mae: 0.3192\n",
            "Epoch 524/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1544 - mae: 0.3106 - val_loss: 0.1584 - val_mae: 0.3136\n",
            "Epoch 525/1000\n",
            "38/38 [==============================] - 1s 14ms/step - loss: 0.1536 - mae: 0.3091 - val_loss: 0.1638 - val_mae: 0.3194\n",
            "Epoch 526/1000\n",
            "38/38 [==============================] - 1s 20ms/step - loss: 0.1550 - mae: 0.3106 - val_loss: 0.1588 - val_mae: 0.3087\n",
            "Epoch 527/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1547 - mae: 0.3100 - val_loss: 0.1592 - val_mae: 0.3085\n",
            "Epoch 528/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1554 - mae: 0.3097 - val_loss: 0.1622 - val_mae: 0.3179\n",
            "Epoch 529/1000\n",
            "38/38 [==============================] - 1s 23ms/step - loss: 0.1530 - mae: 0.3067 - val_loss: 0.1601 - val_mae: 0.3159\n",
            "Epoch 530/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1547 - mae: 0.3102 - val_loss: 0.1580 - val_mae: 0.3130\n",
            "Epoch 531/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1555 - mae: 0.3108 - val_loss: 0.1599 - val_mae: 0.3160\n",
            "Epoch 532/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1553 - mae: 0.3119 - val_loss: 0.1579 - val_mae: 0.3100\n",
            "Epoch 533/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1540 - mae: 0.3092 - val_loss: 0.1577 - val_mae: 0.3122\n",
            "Epoch 534/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1543 - mae: 0.3093 - val_loss: 0.1616 - val_mae: 0.3075\n",
            "Epoch 535/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1542 - mae: 0.3089 - val_loss: 0.1578 - val_mae: 0.3123\n",
            "Epoch 536/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3095 - val_loss: 0.1584 - val_mae: 0.3090\n",
            "Epoch 537/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1540 - mae: 0.3073 - val_loss: 0.1577 - val_mae: 0.3120\n",
            "Epoch 538/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1553 - mae: 0.3103 - val_loss: 0.1583 - val_mae: 0.3135\n",
            "Epoch 539/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3096 - val_loss: 0.1583 - val_mae: 0.3136\n",
            "Epoch 540/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1551 - mae: 0.3109 - val_loss: 0.1580 - val_mae: 0.3089\n",
            "Epoch 541/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3086 - val_loss: 0.1576 - val_mae: 0.3117\n",
            "Epoch 542/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3103 - val_loss: 0.1575 - val_mae: 0.3101\n",
            "Epoch 543/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3087 - val_loss: 0.1575 - val_mae: 0.3108\n",
            "Epoch 544/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3081 - val_loss: 0.1592 - val_mae: 0.3147\n",
            "Epoch 545/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1527 - mae: 0.3064 - val_loss: 0.1608 - val_mae: 0.3160\n",
            "Epoch 546/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3084 - val_loss: 0.1587 - val_mae: 0.3139\n",
            "Epoch 547/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1507 - mae: 0.3056 - val_loss: 0.1712 - val_mae: 0.3079\n",
            "Epoch 548/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3087 - val_loss: 0.1583 - val_mae: 0.3086\n",
            "Epoch 549/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3069 - val_loss: 0.1585 - val_mae: 0.3085\n",
            "Epoch 550/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3069 - val_loss: 0.1593 - val_mae: 0.3147\n",
            "Epoch 551/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3096 - val_loss: 0.1577 - val_mae: 0.3093\n",
            "Epoch 552/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3100 - val_loss: 0.1584 - val_mae: 0.3133\n",
            "Epoch 553/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3090 - val_loss: 0.1684 - val_mae: 0.3230\n",
            "Epoch 554/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1562 - mae: 0.3103 - val_loss: 0.1577 - val_mae: 0.3100\n",
            "Epoch 555/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3057 - val_loss: 0.1584 - val_mae: 0.3135\n",
            "Epoch 556/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3090 - val_loss: 0.1626 - val_mae: 0.3073\n",
            "Epoch 557/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3077 - val_loss: 0.1597 - val_mae: 0.3153\n",
            "Epoch 558/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3110 - val_loss: 0.1590 - val_mae: 0.3146\n",
            "Epoch 559/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3099 - val_loss: 0.1589 - val_mae: 0.3144\n",
            "Epoch 560/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3097 - val_loss: 0.1620 - val_mae: 0.3176\n",
            "Epoch 561/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1561 - mae: 0.3106 - val_loss: 0.1597 - val_mae: 0.3154\n",
            "Epoch 562/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3111 - val_loss: 0.1579 - val_mae: 0.3096\n",
            "Epoch 563/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3073 - val_loss: 0.1587 - val_mae: 0.3085\n",
            "Epoch 564/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3097 - val_loss: 0.1581 - val_mae: 0.3086\n",
            "Epoch 565/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3105 - val_loss: 0.1587 - val_mae: 0.3079\n",
            "Epoch 566/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3094 - val_loss: 0.1581 - val_mae: 0.3126\n",
            "Epoch 567/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3080 - val_loss: 0.1575 - val_mae: 0.3106\n",
            "Epoch 568/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.3093 - val_loss: 0.1574 - val_mae: 0.3099\n",
            "Epoch 569/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1530 - mae: 0.3070 - val_loss: 0.1588 - val_mae: 0.3138\n",
            "Epoch 570/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3077 - val_loss: 0.1577 - val_mae: 0.3120\n",
            "Epoch 571/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3099 - val_loss: 0.1575 - val_mae: 0.3106\n",
            "Epoch 572/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3080 - val_loss: 0.1575 - val_mae: 0.3103\n",
            "Epoch 573/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1556 - mae: 0.3091 - val_loss: 0.1575 - val_mae: 0.3101\n",
            "Epoch 574/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3072 - val_loss: 0.1584 - val_mae: 0.3136\n",
            "Epoch 575/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1551 - mae: 0.3089 - val_loss: 0.1576 - val_mae: 0.3113\n",
            "Epoch 576/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3092 - val_loss: 0.1588 - val_mae: 0.3141\n",
            "Epoch 577/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3096 - val_loss: 0.1584 - val_mae: 0.3084\n",
            "Epoch 578/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3088 - val_loss: 0.1611 - val_mae: 0.3071\n",
            "Epoch 579/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3103 - val_loss: 0.1593 - val_mae: 0.3071\n",
            "Epoch 580/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3096 - val_loss: 0.1594 - val_mae: 0.3146\n",
            "Epoch 581/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3080 - val_loss: 0.1654 - val_mae: 0.3204\n",
            "Epoch 582/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3109 - val_loss: 0.1578 - val_mae: 0.3122\n",
            "Epoch 583/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1566 - mae: 0.3116 - val_loss: 0.1574 - val_mae: 0.3104\n",
            "Epoch 584/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1521 - mae: 0.3059 - val_loss: 0.1669 - val_mae: 0.3068\n",
            "Epoch 585/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3104 - val_loss: 0.1575 - val_mae: 0.3099\n",
            "Epoch 586/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3086 - val_loss: 0.1580 - val_mae: 0.3125\n",
            "Epoch 587/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3090 - val_loss: 0.1592 - val_mae: 0.3074\n",
            "Epoch 588/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1531 - mae: 0.3087 - val_loss: 0.1574 - val_mae: 0.3102\n",
            "Epoch 589/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3082 - val_loss: 0.1595 - val_mae: 0.3071\n",
            "Epoch 590/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3071 - val_loss: 0.1580 - val_mae: 0.3122\n",
            "Epoch 591/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3082 - val_loss: 0.1583 - val_mae: 0.3130\n",
            "Epoch 592/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3069 - val_loss: 0.1612 - val_mae: 0.3066\n",
            "Epoch 593/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3082 - val_loss: 0.1578 - val_mae: 0.3083\n",
            "Epoch 594/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3072 - val_loss: 0.1589 - val_mae: 0.3138\n",
            "Epoch 595/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3083 - val_loss: 0.1578 - val_mae: 0.3120\n",
            "Epoch 596/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1531 - mae: 0.3082 - val_loss: 0.1577 - val_mae: 0.3118\n",
            "Epoch 597/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1542 - mae: 0.3086 - val_loss: 0.1636 - val_mae: 0.3187\n",
            "Epoch 598/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3072 - val_loss: 0.1577 - val_mae: 0.3118\n",
            "Epoch 599/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3099 - val_loss: 0.1577 - val_mae: 0.3118\n",
            "Epoch 600/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3096 - val_loss: 0.1614 - val_mae: 0.3167\n",
            "Epoch 601/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3100 - val_loss: 0.1593 - val_mae: 0.3147\n",
            "Epoch 602/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3101 - val_loss: 0.1580 - val_mae: 0.3125\n",
            "Epoch 603/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3102 - val_loss: 0.1595 - val_mae: 0.3150\n",
            "Epoch 604/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1527 - mae: 0.3069 - val_loss: 0.1615 - val_mae: 0.3074\n",
            "Epoch 605/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3096 - val_loss: 0.1598 - val_mae: 0.3156\n",
            "Epoch 606/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3103 - val_loss: 0.1577 - val_mae: 0.3119\n",
            "Epoch 607/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3097 - val_loss: 0.1576 - val_mae: 0.3109\n",
            "Epoch 608/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3093 - val_loss: 0.1611 - val_mae: 0.3073\n",
            "Epoch 609/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1538 - mae: 0.3085 - val_loss: 0.1593 - val_mae: 0.3076\n",
            "Epoch 610/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1561 - mae: 0.3084 - val_loss: 0.1578 - val_mae: 0.3119\n",
            "Epoch 611/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1530 - mae: 0.3081 - val_loss: 0.1616 - val_mae: 0.3063\n",
            "Epoch 612/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3089 - val_loss: 0.1579 - val_mae: 0.3083\n",
            "Epoch 613/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1552 - mae: 0.3107 - val_loss: 0.1574 - val_mae: 0.3106\n",
            "Epoch 614/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1528 - mae: 0.3072 - val_loss: 0.1597 - val_mae: 0.3070\n",
            "Epoch 615/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1555 - mae: 0.3100 - val_loss: 0.1578 - val_mae: 0.3080\n",
            "Epoch 616/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1536 - mae: 0.3063 - val_loss: 0.1650 - val_mae: 0.3193\n",
            "Epoch 617/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3081 - val_loss: 0.1607 - val_mae: 0.3157\n",
            "Epoch 618/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1548 - mae: 0.3088 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 619/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1533 - mae: 0.3057 - val_loss: 0.1574 - val_mae: 0.3096\n",
            "Epoch 620/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1542 - mae: 0.3085 - val_loss: 0.1595 - val_mae: 0.3146\n",
            "Epoch 621/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1535 - mae: 0.3085 - val_loss: 0.1575 - val_mae: 0.3099\n",
            "Epoch 622/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1548 - mae: 0.3084 - val_loss: 0.1605 - val_mae: 0.3156\n",
            "Epoch 623/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1536 - mae: 0.3083 - val_loss: 0.1684 - val_mae: 0.3224\n",
            "Epoch 624/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1551 - mae: 0.3091 - val_loss: 0.1575 - val_mae: 0.3111\n",
            "Epoch 625/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1541 - mae: 0.3089 - val_loss: 0.1575 - val_mae: 0.3105\n",
            "Epoch 626/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1533 - mae: 0.3086 - val_loss: 0.1612 - val_mae: 0.3066\n",
            "Epoch 627/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1531 - mae: 0.3059 - val_loss: 0.1589 - val_mae: 0.3074\n",
            "Epoch 628/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1537 - mae: 0.3069 - val_loss: 0.1611 - val_mae: 0.3161\n",
            "Epoch 629/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1537 - mae: 0.3081 - val_loss: 0.1603 - val_mae: 0.3153\n",
            "Epoch 630/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1553 - mae: 0.3091 - val_loss: 0.1575 - val_mae: 0.3111\n",
            "Epoch 631/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3091 - val_loss: 0.1583 - val_mae: 0.3080\n",
            "Epoch 632/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1542 - mae: 0.3083 - val_loss: 0.1577 - val_mae: 0.3093\n",
            "Epoch 633/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1537 - mae: 0.3085 - val_loss: 0.1640 - val_mae: 0.3190\n",
            "Epoch 634/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1536 - mae: 0.3097 - val_loss: 0.1602 - val_mae: 0.3071\n",
            "Epoch 635/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1548 - mae: 0.3075 - val_loss: 0.1575 - val_mae: 0.3102\n",
            "Epoch 636/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3091 - val_loss: 0.1577 - val_mae: 0.3116\n",
            "Epoch 637/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3104 - val_loss: 0.1586 - val_mae: 0.3081\n",
            "Epoch 638/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3068 - val_loss: 0.1579 - val_mae: 0.3122\n",
            "Epoch 639/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3084 - val_loss: 0.1581 - val_mae: 0.3126\n",
            "Epoch 640/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3106 - val_loss: 0.1590 - val_mae: 0.3141\n",
            "Epoch 641/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1551 - mae: 0.3098 - val_loss: 0.1575 - val_mae: 0.3108\n",
            "Epoch 642/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3077 - val_loss: 0.1636 - val_mae: 0.3185\n",
            "Epoch 643/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1552 - mae: 0.3109 - val_loss: 0.1580 - val_mae: 0.3125\n",
            "Epoch 644/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1529 - mae: 0.3076 - val_loss: 0.1600 - val_mae: 0.3075\n",
            "Epoch 645/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3093 - val_loss: 0.1634 - val_mae: 0.3184\n",
            "Epoch 646/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3119 - val_loss: 0.1577 - val_mae: 0.3089\n",
            "Epoch 647/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3094 - val_loss: 0.1587 - val_mae: 0.3139\n",
            "Epoch 648/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3079 - val_loss: 0.1639 - val_mae: 0.3190\n",
            "Epoch 649/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3081 - val_loss: 0.1577 - val_mae: 0.3093\n",
            "Epoch 650/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3081 - val_loss: 0.1577 - val_mae: 0.3092\n",
            "Epoch 651/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1533 - mae: 0.3064 - val_loss: 0.1576 - val_mae: 0.3112\n",
            "Epoch 652/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.3103 - val_loss: 0.1583 - val_mae: 0.3086\n",
            "Epoch 653/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3087 - val_loss: 0.1583 - val_mae: 0.3133\n",
            "Epoch 654/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1558 - mae: 0.3103 - val_loss: 0.1576 - val_mae: 0.3112\n",
            "Epoch 655/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3099 - val_loss: 0.1575 - val_mae: 0.3107\n",
            "Epoch 656/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3083 - val_loss: 0.1609 - val_mae: 0.3160\n",
            "Epoch 657/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3099 - val_loss: 0.1598 - val_mae: 0.3076\n",
            "Epoch 658/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3081 - val_loss: 0.1579 - val_mae: 0.3121\n",
            "Epoch 659/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1557 - mae: 0.3102 - val_loss: 0.1578 - val_mae: 0.3119\n",
            "Epoch 660/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3087 - val_loss: 0.1576 - val_mae: 0.3092\n",
            "Epoch 661/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1533 - mae: 0.3065 - val_loss: 0.1611 - val_mae: 0.3158\n",
            "Epoch 662/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3099 - val_loss: 0.1589 - val_mae: 0.3136\n",
            "Epoch 663/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3079 - val_loss: 0.1582 - val_mae: 0.3126\n",
            "Epoch 664/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1532 - mae: 0.3075 - val_loss: 0.1574 - val_mae: 0.3102\n",
            "Epoch 665/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3095 - val_loss: 0.1574 - val_mae: 0.3101\n",
            "Epoch 666/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1532 - mae: 0.3048 - val_loss: 0.1614 - val_mae: 0.3160\n",
            "Epoch 667/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3086 - val_loss: 0.1584 - val_mae: 0.3126\n",
            "Epoch 668/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1556 - mae: 0.3086 - val_loss: 0.1574 - val_mae: 0.3097\n",
            "Epoch 669/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3091 - val_loss: 0.1574 - val_mae: 0.3096\n",
            "Epoch 670/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3078 - val_loss: 0.1582 - val_mae: 0.3076\n",
            "Epoch 671/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1525 - mae: 0.3051 - val_loss: 0.1576 - val_mae: 0.3084\n",
            "Epoch 672/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1532 - mae: 0.3062 - val_loss: 0.1591 - val_mae: 0.3065\n",
            "Epoch 673/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3062 - val_loss: 0.1581 - val_mae: 0.3122\n",
            "Epoch 674/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3086 - val_loss: 0.1578 - val_mae: 0.3115\n",
            "Epoch 675/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1528 - mae: 0.3060 - val_loss: 0.1638 - val_mae: 0.3064\n",
            "Epoch 676/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3068 - val_loss: 0.1579 - val_mae: 0.3081\n",
            "Epoch 677/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1555 - mae: 0.3100 - val_loss: 0.1580 - val_mae: 0.3078\n",
            "Epoch 678/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3061 - val_loss: 0.1585 - val_mae: 0.3131\n",
            "Epoch 679/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3085 - val_loss: 0.1631 - val_mae: 0.3064\n",
            "Epoch 680/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1553 - mae: 0.3089 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 681/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3094 - val_loss: 0.1596 - val_mae: 0.3068\n",
            "Epoch 682/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3058 - val_loss: 0.1703 - val_mae: 0.3233\n",
            "Epoch 683/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3069 - val_loss: 0.1655 - val_mae: 0.3197\n",
            "Epoch 684/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1563 - mae: 0.3104 - val_loss: 0.1583 - val_mae: 0.3128\n",
            "Epoch 685/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3087 - val_loss: 0.1586 - val_mae: 0.3080\n",
            "Epoch 686/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3068 - val_loss: 0.1589 - val_mae: 0.3140\n",
            "Epoch 687/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3083 - val_loss: 0.1616 - val_mae: 0.3167\n",
            "Epoch 688/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1553 - mae: 0.3106 - val_loss: 0.1576 - val_mae: 0.3112\n",
            "Epoch 689/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3076 - val_loss: 0.1577 - val_mae: 0.3116\n",
            "Epoch 690/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1528 - mae: 0.3080 - val_loss: 0.1587 - val_mae: 0.3081\n",
            "Epoch 691/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3083 - val_loss: 0.1586 - val_mae: 0.3083\n",
            "Epoch 692/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3087 - val_loss: 0.1618 - val_mae: 0.3166\n",
            "Epoch 693/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1560 - mae: 0.3090 - val_loss: 0.1575 - val_mae: 0.3104\n",
            "Epoch 694/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3076 - val_loss: 0.1578 - val_mae: 0.3084\n",
            "Epoch 695/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3085 - val_loss: 0.1574 - val_mae: 0.3085\n",
            "Epoch 696/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3071 - val_loss: 0.1574 - val_mae: 0.3085\n",
            "Epoch 697/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1534 - mae: 0.3066 - val_loss: 0.1632 - val_mae: 0.3179\n",
            "Epoch 698/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3097 - val_loss: 0.1647 - val_mae: 0.3192\n",
            "Epoch 699/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3092 - val_loss: 0.1577 - val_mae: 0.3114\n",
            "Epoch 700/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1527 - mae: 0.3068 - val_loss: 0.1575 - val_mae: 0.3107\n",
            "Epoch 701/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3092 - val_loss: 0.1581 - val_mae: 0.3078\n",
            "Epoch 702/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3082 - val_loss: 0.1576 - val_mae: 0.3088\n",
            "Epoch 703/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3072 - val_loss: 0.1618 - val_mae: 0.3164\n",
            "Epoch 704/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3080 - val_loss: 0.1583 - val_mae: 0.3078\n",
            "Epoch 705/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1526 - mae: 0.3060 - val_loss: 0.1624 - val_mae: 0.3168\n",
            "Epoch 706/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1537 - mae: 0.3081 - val_loss: 0.1621 - val_mae: 0.3167\n",
            "Epoch 707/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1538 - mae: 0.3082 - val_loss: 0.1658 - val_mae: 0.3202\n",
            "Epoch 708/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1536 - mae: 0.3095 - val_loss: 0.1575 - val_mae: 0.3090\n",
            "Epoch 709/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1546 - mae: 0.3082 - val_loss: 0.1611 - val_mae: 0.3159\n",
            "Epoch 710/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1548 - mae: 0.3095 - val_loss: 0.1575 - val_mae: 0.3094\n",
            "Epoch 711/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1527 - mae: 0.3071 - val_loss: 0.1607 - val_mae: 0.3071\n",
            "Epoch 712/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1539 - mae: 0.3086 - val_loss: 0.1579 - val_mae: 0.3087\n",
            "Epoch 713/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1534 - mae: 0.3079 - val_loss: 0.1575 - val_mae: 0.3099\n",
            "Epoch 714/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1545 - mae: 0.3090 - val_loss: 0.1576 - val_mae: 0.3110\n",
            "Epoch 715/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1550 - mae: 0.3091 - val_loss: 0.1574 - val_mae: 0.3101\n",
            "Epoch 716/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1518 - mae: 0.3058 - val_loss: 0.1660 - val_mae: 0.3067\n",
            "Epoch 717/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1557 - mae: 0.3095 - val_loss: 0.1586 - val_mae: 0.3132\n",
            "Epoch 718/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1540 - mae: 0.3095 - val_loss: 0.1643 - val_mae: 0.3191\n",
            "Epoch 719/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1537 - mae: 0.3076 - val_loss: 0.1721 - val_mae: 0.3249\n",
            "Epoch 720/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1555 - mae: 0.3100 - val_loss: 0.1578 - val_mae: 0.3118\n",
            "Epoch 721/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1549 - mae: 0.3087 - val_loss: 0.1584 - val_mae: 0.3130\n",
            "Epoch 722/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1540 - mae: 0.3093 - val_loss: 0.1581 - val_mae: 0.3081\n",
            "Epoch 723/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1522 - mae: 0.3054 - val_loss: 0.1575 - val_mae: 0.3088\n",
            "Epoch 724/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1550 - mae: 0.3093 - val_loss: 0.1574 - val_mae: 0.3096\n",
            "Epoch 725/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1524 - mae: 0.3038 - val_loss: 0.1594 - val_mae: 0.3139\n",
            "Epoch 726/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3080 - val_loss: 0.1603 - val_mae: 0.3149\n",
            "Epoch 727/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1546 - mae: 0.3089 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 728/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1546 - mae: 0.3079 - val_loss: 0.1580 - val_mae: 0.3120\n",
            "Epoch 729/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1550 - mae: 0.3084 - val_loss: 0.1584 - val_mae: 0.3127\n",
            "Epoch 730/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1541 - mae: 0.3092 - val_loss: 0.1576 - val_mae: 0.3086\n",
            "Epoch 731/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1548 - mae: 0.3080 - val_loss: 0.1583 - val_mae: 0.3123\n",
            "Epoch 732/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1541 - mae: 0.3090 - val_loss: 0.1577 - val_mae: 0.3112\n",
            "Epoch 733/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3062 - val_loss: 0.1592 - val_mae: 0.3138\n",
            "Epoch 734/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3081 - val_loss: 0.1596 - val_mae: 0.3142\n",
            "Epoch 735/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3082 - val_loss: 0.1576 - val_mae: 0.3108\n",
            "Epoch 736/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3095 - val_loss: 0.1575 - val_mae: 0.3095\n",
            "Epoch 737/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3090 - val_loss: 0.1586 - val_mae: 0.3131\n",
            "Epoch 738/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3082 - val_loss: 0.1605 - val_mae: 0.3152\n",
            "Epoch 739/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1527 - mae: 0.3056 - val_loss: 0.1587 - val_mae: 0.3132\n",
            "Epoch 740/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1553 - mae: 0.3090 - val_loss: 0.1577 - val_mae: 0.3084\n",
            "Epoch 741/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3103 - val_loss: 0.1576 - val_mae: 0.3109\n",
            "Epoch 742/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3081 - val_loss: 0.1606 - val_mae: 0.3148\n",
            "Epoch 743/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3090 - val_loss: 0.1574 - val_mae: 0.3091\n",
            "Epoch 744/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3089 - val_loss: 0.1592 - val_mae: 0.3137\n",
            "Epoch 745/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1537 - mae: 0.3077 - val_loss: 0.1585 - val_mae: 0.3128\n",
            "Epoch 746/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3090 - val_loss: 0.1591 - val_mae: 0.3136\n",
            "Epoch 747/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3088 - val_loss: 0.1604 - val_mae: 0.3150\n",
            "Epoch 748/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3080 - val_loss: 0.1591 - val_mae: 0.3136\n",
            "Epoch 749/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1539 - mae: 0.3069 - val_loss: 0.1613 - val_mae: 0.3158\n",
            "Epoch 750/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1532 - mae: 0.3093 - val_loss: 0.1576 - val_mae: 0.3108\n",
            "Epoch 751/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1549 - mae: 0.3096 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 752/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3077 - val_loss: 0.1594 - val_mae: 0.3067\n",
            "Epoch 753/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3091 - val_loss: 0.1619 - val_mae: 0.3162\n",
            "Epoch 754/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3085 - val_loss: 0.1601 - val_mae: 0.3146\n",
            "Epoch 755/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3081 - val_loss: 0.1578 - val_mae: 0.3079\n",
            "Epoch 756/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3079 - val_loss: 0.1596 - val_mae: 0.3067\n",
            "Epoch 757/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3082 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 758/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1506 - mae: 0.3050 - val_loss: 0.1572 - val_mae: 0.3088\n",
            "Epoch 759/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3053 - val_loss: 0.1573 - val_mae: 0.3094\n",
            "Epoch 760/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3079 - val_loss: 0.1574 - val_mae: 0.3089\n",
            "Epoch 761/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3087 - val_loss: 0.1623 - val_mae: 0.3165\n",
            "Epoch 762/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3077 - val_loss: 0.1578 - val_mae: 0.3115\n",
            "Epoch 763/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3085 - val_loss: 0.1617 - val_mae: 0.3061\n",
            "Epoch 764/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1522 - mae: 0.3057 - val_loss: 0.1596 - val_mae: 0.3142\n",
            "Epoch 765/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3092 - val_loss: 0.1631 - val_mae: 0.3175\n",
            "Epoch 766/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1551 - mae: 0.3110 - val_loss: 0.1575 - val_mae: 0.3090\n",
            "Epoch 767/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3093 - val_loss: 0.1580 - val_mae: 0.3118\n",
            "Epoch 768/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3093 - val_loss: 0.1587 - val_mae: 0.3130\n",
            "Epoch 769/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1515 - mae: 0.3062 - val_loss: 0.1580 - val_mae: 0.3075\n",
            "Epoch 770/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1559 - mae: 0.3097 - val_loss: 0.1584 - val_mae: 0.3122\n",
            "Epoch 771/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1552 - mae: 0.3089 - val_loss: 0.1578 - val_mae: 0.3075\n",
            "Epoch 772/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3073 - val_loss: 0.1599 - val_mae: 0.3141\n",
            "Epoch 773/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3084 - val_loss: 0.1642 - val_mae: 0.3182\n",
            "Epoch 774/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3096 - val_loss: 0.1573 - val_mae: 0.3096\n",
            "Epoch 775/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1556 - mae: 0.3098 - val_loss: 0.1589 - val_mae: 0.3128\n",
            "Epoch 776/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3073 - val_loss: 0.1600 - val_mae: 0.3141\n",
            "Epoch 777/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1531 - mae: 0.3064 - val_loss: 0.1574 - val_mae: 0.3082\n",
            "Epoch 778/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1538 - mae: 0.3084 - val_loss: 0.1574 - val_mae: 0.3080\n",
            "Epoch 779/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3061 - val_loss: 0.1591 - val_mae: 0.3129\n",
            "Epoch 780/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3061 - val_loss: 0.1573 - val_mae: 0.3097\n",
            "Epoch 781/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1518 - mae: 0.3045 - val_loss: 0.1606 - val_mae: 0.3145\n",
            "Epoch 782/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3090 - val_loss: 0.1573 - val_mae: 0.3095\n",
            "Epoch 783/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3066 - val_loss: 0.1574 - val_mae: 0.3082\n",
            "Epoch 784/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1553 - mae: 0.3091 - val_loss: 0.1573 - val_mae: 0.3089\n",
            "Epoch 785/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1554 - mae: 0.3080 - val_loss: 0.1587 - val_mae: 0.3125\n",
            "Epoch 786/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3081 - val_loss: 0.1594 - val_mae: 0.3058\n",
            "Epoch 787/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1526 - mae: 0.3084 - val_loss: 0.1580 - val_mae: 0.3065\n",
            "Epoch 788/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3080 - val_loss: 0.1574 - val_mae: 0.3098\n",
            "Epoch 789/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3077 - val_loss: 0.1576 - val_mae: 0.3107\n",
            "Epoch 790/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3062 - val_loss: 0.1590 - val_mae: 0.3128\n",
            "Epoch 791/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3072 - val_loss: 0.1574 - val_mae: 0.3101\n",
            "Epoch 792/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3067 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 793/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1513 - mae: 0.3052 - val_loss: 0.1618 - val_mae: 0.3155\n",
            "Epoch 794/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1539 - mae: 0.3076 - val_loss: 0.1658 - val_mae: 0.3194\n",
            "Epoch 795/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3078 - val_loss: 0.1574 - val_mae: 0.3094\n",
            "Epoch 796/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3076 - val_loss: 0.1591 - val_mae: 0.3132\n",
            "Epoch 797/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3073 - val_loss: 0.1584 - val_mae: 0.3121\n",
            "Epoch 798/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1524 - mae: 0.3062 - val_loss: 0.1609 - val_mae: 0.3148\n",
            "Epoch 799/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1550 - mae: 0.3095 - val_loss: 0.1575 - val_mae: 0.3105\n",
            "Epoch 800/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1539 - mae: 0.3075 - val_loss: 0.1574 - val_mae: 0.3099\n",
            "Epoch 801/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1532 - mae: 0.3075 - val_loss: 0.1603 - val_mae: 0.3148\n",
            "Epoch 802/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1541 - mae: 0.3085 - val_loss: 0.1592 - val_mae: 0.3136\n",
            "Epoch 803/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1547 - mae: 0.3073 - val_loss: 0.1589 - val_mae: 0.3131\n",
            "Epoch 804/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1541 - mae: 0.3084 - val_loss: 0.1625 - val_mae: 0.3162\n",
            "Epoch 805/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1524 - mae: 0.3071 - val_loss: 0.1619 - val_mae: 0.3055\n",
            "Epoch 806/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1551 - mae: 0.3091 - val_loss: 0.1573 - val_mae: 0.3090\n",
            "Epoch 807/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1548 - mae: 0.3065 - val_loss: 0.1609 - val_mae: 0.3149\n",
            "Epoch 808/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1542 - mae: 0.3078 - val_loss: 0.1598 - val_mae: 0.3140\n",
            "Epoch 809/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1537 - mae: 0.3061 - val_loss: 0.1603 - val_mae: 0.3142\n",
            "Epoch 810/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1539 - mae: 0.3089 - val_loss: 0.1580 - val_mae: 0.3116\n",
            "Epoch 811/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1548 - mae: 0.3079 - val_loss: 0.1594 - val_mae: 0.3066\n",
            "Epoch 812/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1532 - mae: 0.3067 - val_loss: 0.1588 - val_mae: 0.3069\n",
            "Epoch 813/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1540 - mae: 0.3083 - val_loss: 0.1575 - val_mae: 0.3105\n",
            "Epoch 814/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1541 - mae: 0.3090 - val_loss: 0.1651 - val_mae: 0.3189\n",
            "Epoch 815/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1540 - mae: 0.3088 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 816/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1536 - mae: 0.3080 - val_loss: 0.1597 - val_mae: 0.3060\n",
            "Epoch 817/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1541 - mae: 0.3073 - val_loss: 0.1595 - val_mae: 0.3063\n",
            "Epoch 818/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1535 - mae: 0.3059 - val_loss: 0.1586 - val_mae: 0.3126\n",
            "Epoch 819/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1545 - mae: 0.3080 - val_loss: 0.1574 - val_mae: 0.3092\n",
            "Epoch 820/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1533 - mae: 0.3071 - val_loss: 0.1573 - val_mae: 0.3095\n",
            "Epoch 821/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1532 - mae: 0.3074 - val_loss: 0.1628 - val_mae: 0.3166\n",
            "Epoch 822/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1556 - mae: 0.3091 - val_loss: 0.1588 - val_mae: 0.3128\n",
            "Epoch 823/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1531 - mae: 0.3067 - val_loss: 0.1601 - val_mae: 0.3062\n",
            "Epoch 824/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1562 - mae: 0.3078 - val_loss: 0.1574 - val_mae: 0.3100\n",
            "Epoch 825/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3066 - val_loss: 0.1579 - val_mae: 0.3073\n",
            "Epoch 826/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1531 - mae: 0.3074 - val_loss: 0.1623 - val_mae: 0.3160\n",
            "Epoch 827/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1529 - mae: 0.3064 - val_loss: 0.1582 - val_mae: 0.3117\n",
            "Epoch 828/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3095 - val_loss: 0.1577 - val_mae: 0.3108\n",
            "Epoch 829/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1528 - mae: 0.3057 - val_loss: 0.1665 - val_mae: 0.3197\n",
            "Epoch 830/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3070 - val_loss: 0.1574 - val_mae: 0.3082\n",
            "Epoch 831/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1532 - mae: 0.3067 - val_loss: 0.1582 - val_mae: 0.3066\n",
            "Epoch 832/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3076 - val_loss: 0.1601 - val_mae: 0.3137\n",
            "Epoch 833/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3080 - val_loss: 0.1659 - val_mae: 0.3054\n",
            "Epoch 834/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1563 - mae: 0.3061 - val_loss: 0.1580 - val_mae: 0.3112\n",
            "Epoch 835/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3072 - val_loss: 0.1573 - val_mae: 0.3091\n",
            "Epoch 836/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3077 - val_loss: 0.1575 - val_mae: 0.3103\n",
            "Epoch 837/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1536 - mae: 0.3071 - val_loss: 0.1601 - val_mae: 0.3142\n",
            "Epoch 838/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3077 - val_loss: 0.1590 - val_mae: 0.3131\n",
            "Epoch 839/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1535 - mae: 0.3060 - val_loss: 0.1668 - val_mae: 0.3199\n",
            "Epoch 840/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3082 - val_loss: 0.1600 - val_mae: 0.3142\n",
            "Epoch 841/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3088 - val_loss: 0.1579 - val_mae: 0.3078\n",
            "Epoch 842/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3081 - val_loss: 0.1574 - val_mae: 0.3094\n",
            "Epoch 843/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1522 - mae: 0.3055 - val_loss: 0.1652 - val_mae: 0.3189\n",
            "Epoch 844/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3085 - val_loss: 0.1577 - val_mae: 0.3108\n",
            "Epoch 845/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3080 - val_loss: 0.1578 - val_mae: 0.3112\n",
            "Epoch 846/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3071 - val_loss: 0.1574 - val_mae: 0.3082\n",
            "Epoch 847/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3066 - val_loss: 0.1573 - val_mae: 0.3092\n",
            "Epoch 848/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3077 - val_loss: 0.1577 - val_mae: 0.3109\n",
            "Epoch 849/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1534 - mae: 0.3072 - val_loss: 0.1573 - val_mae: 0.3092\n",
            "Epoch 850/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1532 - mae: 0.3057 - val_loss: 0.1575 - val_mae: 0.3102\n",
            "Epoch 851/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1539 - mae: 0.3079 - val_loss: 0.1585 - val_mae: 0.3064\n",
            "Epoch 852/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3078 - val_loss: 0.1606 - val_mae: 0.3142\n",
            "Epoch 853/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3076 - val_loss: 0.1573 - val_mae: 0.3092\n",
            "Epoch 854/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3087 - val_loss: 0.1588 - val_mae: 0.3065\n",
            "Epoch 855/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3080 - val_loss: 0.1601 - val_mae: 0.3058\n",
            "Epoch 856/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3070 - val_loss: 0.1574 - val_mae: 0.3083\n",
            "Epoch 857/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3075 - val_loss: 0.1573 - val_mae: 0.3091\n",
            "Epoch 858/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1529 - mae: 0.3080 - val_loss: 0.1580 - val_mae: 0.3070\n",
            "Epoch 859/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3085 - val_loss: 0.1576 - val_mae: 0.3105\n",
            "Epoch 860/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3079 - val_loss: 0.1579 - val_mae: 0.3074\n",
            "Epoch 861/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1532 - mae: 0.3069 - val_loss: 0.1575 - val_mae: 0.3081\n",
            "Epoch 862/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1550 - mae: 0.3095 - val_loss: 0.1577 - val_mae: 0.3077\n",
            "Epoch 863/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3066 - val_loss: 0.1575 - val_mae: 0.3085\n",
            "Epoch 864/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3072 - val_loss: 0.1576 - val_mae: 0.3080\n",
            "Epoch 865/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3086 - val_loss: 0.1574 - val_mae: 0.3092\n",
            "Epoch 866/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3076 - val_loss: 0.1590 - val_mae: 0.3130\n",
            "Epoch 867/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1517 - mae: 0.3072 - val_loss: 0.1598 - val_mae: 0.3063\n",
            "Epoch 868/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3062 - val_loss: 0.1594 - val_mae: 0.3134\n",
            "Epoch 869/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3087 - val_loss: 0.1577 - val_mae: 0.3107\n",
            "Epoch 870/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1522 - mae: 0.3069 - val_loss: 0.1647 - val_mae: 0.3055\n",
            "Epoch 871/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1536 - mae: 0.3073 - val_loss: 0.1582 - val_mae: 0.3071\n",
            "Epoch 872/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3053 - val_loss: 0.1585 - val_mae: 0.3123\n",
            "Epoch 873/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3093 - val_loss: 0.1598 - val_mae: 0.3061\n",
            "Epoch 874/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1522 - mae: 0.3060 - val_loss: 0.1591 - val_mae: 0.3068\n",
            "Epoch 875/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1532 - mae: 0.3072 - val_loss: 0.1577 - val_mae: 0.3080\n",
            "Epoch 876/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3081 - val_loss: 0.1586 - val_mae: 0.3123\n",
            "Epoch 877/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1546 - mae: 0.3074 - val_loss: 0.1573 - val_mae: 0.3093\n",
            "Epoch 878/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3076 - val_loss: 0.1577 - val_mae: 0.3108\n",
            "Epoch 879/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1542 - mae: 0.3084 - val_loss: 0.1579 - val_mae: 0.3111\n",
            "Epoch 880/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3083 - val_loss: 0.1584 - val_mae: 0.3072\n",
            "Epoch 881/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3067 - val_loss: 0.1598 - val_mae: 0.3139\n",
            "Epoch 882/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3086 - val_loss: 0.1591 - val_mae: 0.3068\n",
            "Epoch 883/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1533 - mae: 0.3067 - val_loss: 0.1589 - val_mae: 0.3067\n",
            "Epoch 884/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1547 - mae: 0.3071 - val_loss: 0.1585 - val_mae: 0.3121\n",
            "Epoch 885/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1533 - mae: 0.3074 - val_loss: 0.1627 - val_mae: 0.3056\n",
            "Epoch 886/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3054 - val_loss: 0.1634 - val_mae: 0.3170\n",
            "Epoch 887/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3075 - val_loss: 0.1581 - val_mae: 0.3067\n",
            "Epoch 888/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3076 - val_loss: 0.1583 - val_mae: 0.3066\n",
            "Epoch 889/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3078 - val_loss: 0.1574 - val_mae: 0.3097\n",
            "Epoch 890/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3066 - val_loss: 0.1578 - val_mae: 0.3072\n",
            "Epoch 891/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1538 - mae: 0.3068 - val_loss: 0.1663 - val_mae: 0.3194\n",
            "Epoch 892/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3074 - val_loss: 0.1612 - val_mae: 0.3151\n",
            "Epoch 893/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1543 - mae: 0.3073 - val_loss: 0.1574 - val_mae: 0.3099\n",
            "Epoch 894/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1542 - mae: 0.3068 - val_loss: 0.1603 - val_mae: 0.3140\n",
            "Epoch 895/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1533 - mae: 0.3054 - val_loss: 0.1643 - val_mae: 0.3176\n",
            "Epoch 896/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1543 - mae: 0.3072 - val_loss: 0.1595 - val_mae: 0.3133\n",
            "Epoch 897/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1544 - mae: 0.3076 - val_loss: 0.1585 - val_mae: 0.3123\n",
            "Epoch 898/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1541 - mae: 0.3071 - val_loss: 0.1574 - val_mae: 0.3097\n",
            "Epoch 899/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1535 - mae: 0.3061 - val_loss: 0.1588 - val_mae: 0.3126\n",
            "Epoch 900/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1540 - mae: 0.3086 - val_loss: 0.1594 - val_mae: 0.3134\n",
            "Epoch 901/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3071 - val_loss: 0.1590 - val_mae: 0.3128\n",
            "Epoch 902/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1538 - mae: 0.3075 - val_loss: 0.1618 - val_mae: 0.3152\n",
            "Epoch 903/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1534 - mae: 0.3075 - val_loss: 0.1619 - val_mae: 0.3151\n",
            "Epoch 904/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1549 - mae: 0.3085 - val_loss: 0.1574 - val_mae: 0.3076\n",
            "Epoch 905/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1538 - mae: 0.3074 - val_loss: 0.1573 - val_mae: 0.3085\n",
            "Epoch 906/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1545 - mae: 0.3059 - val_loss: 0.1601 - val_mae: 0.3137\n",
            "Epoch 907/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1529 - mae: 0.3077 - val_loss: 0.1578 - val_mae: 0.3109\n",
            "Epoch 908/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1537 - mae: 0.3076 - val_loss: 0.1586 - val_mae: 0.3069\n",
            "Epoch 909/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1544 - mae: 0.3077 - val_loss: 0.1574 - val_mae: 0.3096\n",
            "Epoch 910/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1541 - mae: 0.3080 - val_loss: 0.1581 - val_mae: 0.3069\n",
            "Epoch 911/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3068 - val_loss: 0.1586 - val_mae: 0.3121\n",
            "Epoch 912/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1535 - mae: 0.3068 - val_loss: 0.1575 - val_mae: 0.3101\n",
            "Epoch 913/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1543 - mae: 0.3080 - val_loss: 0.1574 - val_mae: 0.3090\n",
            "Epoch 914/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1553 - mae: 0.3090 - val_loss: 0.1580 - val_mae: 0.3074\n",
            "Epoch 915/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1540 - mae: 0.3087 - val_loss: 0.1581 - val_mae: 0.3115\n",
            "Epoch 916/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1550 - mae: 0.3092 - val_loss: 0.1574 - val_mae: 0.3081\n",
            "Epoch 917/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1546 - mae: 0.3082 - val_loss: 0.1574 - val_mae: 0.3084\n",
            "Epoch 918/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1535 - mae: 0.3069 - val_loss: 0.1670 - val_mae: 0.3201\n",
            "Epoch 919/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1551 - mae: 0.3080 - val_loss: 0.1677 - val_mae: 0.3205\n",
            "Epoch 920/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1541 - mae: 0.3072 - val_loss: 0.1577 - val_mae: 0.3108\n",
            "Epoch 921/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1516 - mae: 0.3035 - val_loss: 0.1574 - val_mae: 0.3087\n",
            "Epoch 922/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1546 - mae: 0.3072 - val_loss: 0.1574 - val_mae: 0.3091\n",
            "Epoch 923/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3071 - val_loss: 0.1608 - val_mae: 0.3149\n",
            "Epoch 924/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3086 - val_loss: 0.1576 - val_mae: 0.3087\n",
            "Epoch 925/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3082 - val_loss: 0.1586 - val_mae: 0.3124\n",
            "Epoch 926/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1532 - mae: 0.3089 - val_loss: 0.1581 - val_mae: 0.3118\n",
            "Epoch 927/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3081 - val_loss: 0.1578 - val_mae: 0.3093\n",
            "Epoch 928/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3098 - val_loss: 0.1590 - val_mae: 0.3075\n",
            "Epoch 929/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1547 - mae: 0.3074 - val_loss: 0.1610 - val_mae: 0.3154\n",
            "Epoch 930/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3089 - val_loss: 0.1599 - val_mae: 0.3143\n",
            "Epoch 931/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1541 - mae: 0.3086 - val_loss: 0.1623 - val_mae: 0.3063\n",
            "Epoch 932/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1549 - mae: 0.3085 - val_loss: 0.1577 - val_mae: 0.3111\n",
            "Epoch 933/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1522 - mae: 0.3060 - val_loss: 0.1594 - val_mae: 0.3061\n",
            "Epoch 934/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1549 - mae: 0.3077 - val_loss: 0.1573 - val_mae: 0.3091\n",
            "Epoch 935/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1530 - mae: 0.3058 - val_loss: 0.1689 - val_mae: 0.3214\n",
            "Epoch 936/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1541 - mae: 0.3094 - val_loss: 0.1577 - val_mae: 0.3110\n",
            "Epoch 937/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3088 - val_loss: 0.1605 - val_mae: 0.3147\n",
            "Epoch 938/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3063 - val_loss: 0.1695 - val_mae: 0.3220\n",
            "Epoch 939/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3094 - val_loss: 0.1597 - val_mae: 0.3062\n",
            "Epoch 940/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3075 - val_loss: 0.1588 - val_mae: 0.3124\n",
            "Epoch 941/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1553 - mae: 0.3096 - val_loss: 0.1589 - val_mae: 0.3126\n",
            "Epoch 942/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1534 - mae: 0.3063 - val_loss: 0.1660 - val_mae: 0.3192\n",
            "Epoch 943/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1534 - mae: 0.3063 - val_loss: 0.1574 - val_mae: 0.3086\n",
            "Epoch 944/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3081 - val_loss: 0.1577 - val_mae: 0.3073\n",
            "Epoch 945/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1534 - mae: 0.3071 - val_loss: 0.1576 - val_mae: 0.3075\n",
            "Epoch 946/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3079 - val_loss: 0.1604 - val_mae: 0.3143\n",
            "Epoch 947/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.3074 - val_loss: 0.1576 - val_mae: 0.3106\n",
            "Epoch 948/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1545 - mae: 0.3092 - val_loss: 0.1583 - val_mae: 0.3072\n",
            "Epoch 949/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3068 - val_loss: 0.1577 - val_mae: 0.3079\n",
            "Epoch 950/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1545 - mae: 0.3073 - val_loss: 0.1577 - val_mae: 0.3107\n",
            "Epoch 951/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1548 - mae: 0.3078 - val_loss: 0.1579 - val_mae: 0.3115\n",
            "Epoch 952/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3075 - val_loss: 0.1575 - val_mae: 0.3103\n",
            "Epoch 953/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1533 - mae: 0.3067 - val_loss: 0.1599 - val_mae: 0.3065\n",
            "Epoch 954/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1544 - mae: 0.3072 - val_loss: 0.1577 - val_mae: 0.3108\n",
            "Epoch 955/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1519 - mae: 0.3057 - val_loss: 0.1589 - val_mae: 0.3124\n",
            "Epoch 956/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1523 - mae: 0.3070 - val_loss: 0.1619 - val_mae: 0.3154\n",
            "Epoch 957/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1548 - mae: 0.3090 - val_loss: 0.1575 - val_mae: 0.3088\n",
            "Epoch 958/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3073 - val_loss: 0.1600 - val_mae: 0.3141\n",
            "Epoch 959/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1527 - mae: 0.3080 - val_loss: 0.1670 - val_mae: 0.3057\n",
            "Epoch 960/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1550 - mae: 0.3068 - val_loss: 0.1585 - val_mae: 0.3064\n",
            "Epoch 961/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3068 - val_loss: 0.1574 - val_mae: 0.3094\n",
            "Epoch 962/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1540 - mae: 0.3080 - val_loss: 0.1574 - val_mae: 0.3089\n",
            "Epoch 963/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1537 - mae: 0.3077 - val_loss: 0.1577 - val_mae: 0.3105\n",
            "Epoch 964/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1548 - mae: 0.3086 - val_loss: 0.1592 - val_mae: 0.3062\n",
            "Epoch 965/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3061 - val_loss: 0.1578 - val_mae: 0.3109\n",
            "Epoch 966/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1528 - mae: 0.3063 - val_loss: 0.1623 - val_mae: 0.3161\n",
            "Epoch 967/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1553 - mae: 0.3096 - val_loss: 0.1575 - val_mae: 0.3100\n",
            "Epoch 968/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1535 - mae: 0.3069 - val_loss: 0.1575 - val_mae: 0.3104\n",
            "Epoch 969/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3084 - val_loss: 0.1574 - val_mae: 0.3094\n",
            "Epoch 970/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3083 - val_loss: 0.1591 - val_mae: 0.3133\n",
            "Epoch 971/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3087 - val_loss: 0.1602 - val_mae: 0.3065\n",
            "Epoch 972/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1539 - mae: 0.3085 - val_loss: 0.1576 - val_mae: 0.3086\n",
            "Epoch 973/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1543 - mae: 0.3071 - val_loss: 0.1586 - val_mae: 0.3075\n",
            "Epoch 974/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1538 - mae: 0.3080 - val_loss: 0.1598 - val_mae: 0.3141\n",
            "Epoch 975/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.3092 - val_loss: 0.1596 - val_mae: 0.3138\n",
            "Epoch 976/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3079 - val_loss: 0.1578 - val_mae: 0.3112\n",
            "Epoch 977/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3086 - val_loss: 0.1590 - val_mae: 0.3126\n",
            "Epoch 978/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1528 - mae: 0.3048 - val_loss: 0.1606 - val_mae: 0.3142\n",
            "Epoch 979/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3095 - val_loss: 0.1576 - val_mae: 0.3102\n",
            "Epoch 980/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1544 - mae: 0.3073 - val_loss: 0.1592 - val_mae: 0.3062\n",
            "Epoch 981/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1538 - mae: 0.3075 - val_loss: 0.1588 - val_mae: 0.3065\n",
            "Epoch 982/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1540 - mae: 0.3056 - val_loss: 0.1594 - val_mae: 0.3064\n",
            "Epoch 983/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1552 - mae: 0.3080 - val_loss: 0.1591 - val_mae: 0.3129\n",
            "Epoch 984/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.3074 - val_loss: 0.1586 - val_mae: 0.3124\n",
            "Epoch 985/1000\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1543 - mae: 0.3079 - val_loss: 0.1623 - val_mae: 0.3158\n",
            "Epoch 986/1000\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1555 - mae: 0.3097 - val_loss: 0.1578 - val_mae: 0.3069\n",
            "Epoch 987/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1543 - mae: 0.3074 - val_loss: 0.1671 - val_mae: 0.3199\n",
            "Epoch 988/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1542 - mae: 0.3075 - val_loss: 0.1598 - val_mae: 0.3065\n",
            "Epoch 989/1000\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.1543 - mae: 0.3071 - val_loss: 0.1618 - val_mae: 0.3153\n",
            "Epoch 990/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1531 - mae: 0.3067 - val_loss: 0.1578 - val_mae: 0.3073\n",
            "Epoch 991/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1550 - mae: 0.3073 - val_loss: 0.1573 - val_mae: 0.3090\n",
            "Epoch 992/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1537 - mae: 0.3065 - val_loss: 0.1591 - val_mae: 0.3128\n",
            "Epoch 993/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1533 - mae: 0.3066 - val_loss: 0.1586 - val_mae: 0.3062\n",
            "Epoch 994/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1536 - mae: 0.3086 - val_loss: 0.1576 - val_mae: 0.3076\n",
            "Epoch 995/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1532 - mae: 0.3071 - val_loss: 0.1648 - val_mae: 0.3182\n",
            "Epoch 996/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1547 - mae: 0.3077 - val_loss: 0.1595 - val_mae: 0.3062\n",
            "Epoch 997/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1539 - mae: 0.3065 - val_loss: 0.1594 - val_mae: 0.3131\n",
            "Epoch 998/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1538 - mae: 0.3068 - val_loss: 0.1575 - val_mae: 0.3099\n",
            "Epoch 999/1000\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.1540 - mae: 0.3071 - val_loss: 0.1656 - val_mae: 0.3187\n",
            "Epoch 1000/1000\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1551 - mae: 0.3082 - val_loss: 0.1586 - val_mae: 0.3120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2、绘制历史数据"
      ],
      "metadata": {
        "id": "NV2Iz72URKDH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history_1.history['loss']\n",
        "val_loss = history_1.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "U_mCU7JvI-MM",
        "outputId": "246df3b1-3d0f-4860-d02c-2feeced804c3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "跳过前一百轮次"
      ],
      "metadata": {
        "id": "vah0x_IiSmS0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exclude the first few epochs so the graph is easier to read\n",
        "SKIP = 100\n",
        "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
        "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V19HZ1erJPPY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "3539d9d7-f56d-453d-c2ba-4b4a5b2b31ff"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "绘制平均绝对误差"
      ],
      "metadata": {
        "id": "Q3NLp_W-TDn6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw a graph of mean absolute error, which is another way of\n",
        "# measuring the amount of error in the prediction.\n",
        "mae = history_1.history['mae']\n",
        "val_mae = history_1.history['val_mae']\n",
        "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
        "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
        "plt.title('Training and validation mean absolute error')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "S9mno9PcNJxl",
        "outputId": "c4711a83-2845-42e4-d832-ce3f71587659"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "绘制训练数据的预测值与期望值"
      ],
      "metadata": {
        "id": "YcZe1pq5TOOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the model to make predictions from our validation data\n",
        "predictions = model_1.predict(x_train)\n",
        "# Plot the predictions along with the test data\n",
        "plt.clf()\n",
        "plt.title('Training data predicted vs actual values')\n",
        "plt.plot(x_test, y_test, 'b.', label='Actual')\n",
        "plt.plot(x_train, predictions, 'r.', label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "bemlF0k1TIuJ",
        "outputId": "f26b865f-8488-43ff-ed92-b22943dbc684"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3、改进我们的模型-----在中间加一层包含16个神经元的层。"
      ],
      "metadata": {
        "id": "24E_ruwjTq7q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = tf.keras.Sequential()\n",
        "# First layer takes a scalar input and feeds it through 16 \"neurons.\" The\n",
        "# neurons decide whether to activate based on the 'relu' activation function.\n",
        "model_2.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n",
        "# The new second layer may help the network learn more complex representations\n",
        "model_2.add(layers.Dense(16, activation='relu'))\n",
        "# Final layer is a single neuron, since we want to output a single value\n",
        "model_2.add(layers.Dense(1))\n",
        "# Compile the model using a standard optimizer and loss function for regression\n",
        "model_2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
        "# Show a summary of the model\n",
        "model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnNODZ7jTdJ1",
        "outputId": "de2e1159-bd7a-40e1-938d-9fffae791087"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 16)                32        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 16)                272       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 321 (1.25 KB)\n",
            "Trainable params: 321 (1.25 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "训练模型"
      ],
      "metadata": {
        "id": "ZH8n-TxaUwQz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_2 = model_2.fit(x_train, y_train, epochs=600, batch_size=16,\n",
        " validation_data=(x_validate, y_validate))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXPtEXttUSwA",
        "outputId": "bf03834b-4984-4591-822f-6928e603e807"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/600\n",
            "38/38 [==============================] - 1s 12ms/step - loss: 0.6213 - mae: 0.6976 - val_loss: 0.4919 - val_mae: 0.6207\n",
            "Epoch 2/600\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.4293 - mae: 0.5722 - val_loss: 0.4361 - val_mae: 0.5725\n",
            "Epoch 3/600\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.3928 - mae: 0.5423 - val_loss: 0.4007 - val_mae: 0.5518\n",
            "Epoch 4/600\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.3610 - mae: 0.5216 - val_loss: 0.3626 - val_mae: 0.5213\n",
            "Epoch 5/600\n",
            "38/38 [==============================] - 0s 13ms/step - loss: 0.3212 - mae: 0.4939 - val_loss: 0.3151 - val_mae: 0.4816\n",
            "Epoch 6/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.2795 - mae: 0.4608 - val_loss: 0.2783 - val_mae: 0.4500\n",
            "Epoch 7/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.2451 - mae: 0.4317 - val_loss: 0.2453 - val_mae: 0.4258\n",
            "Epoch 8/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.2178 - mae: 0.4101 - val_loss: 0.2177 - val_mae: 0.3998\n",
            "Epoch 9/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1953 - mae: 0.3879 - val_loss: 0.2000 - val_mae: 0.3873\n",
            "Epoch 10/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1785 - mae: 0.3701 - val_loss: 0.1841 - val_mae: 0.3711\n",
            "Epoch 11/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.1685 - mae: 0.3590 - val_loss: 0.1704 - val_mae: 0.3558\n",
            "Epoch 12/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1605 - mae: 0.3456 - val_loss: 0.1655 - val_mae: 0.3495\n",
            "Epoch 13/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1565 - mae: 0.3389 - val_loss: 0.1590 - val_mae: 0.3403\n",
            "Epoch 14/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1514 - mae: 0.3315 - val_loss: 0.1559 - val_mae: 0.3309\n",
            "Epoch 15/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1483 - mae: 0.3251 - val_loss: 0.1492 - val_mae: 0.3245\n",
            "Epoch 16/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1443 - mae: 0.3186 - val_loss: 0.1481 - val_mae: 0.3237\n",
            "Epoch 17/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1423 - mae: 0.3149 - val_loss: 0.1436 - val_mae: 0.3166\n",
            "Epoch 18/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.1386 - mae: 0.3082 - val_loss: 0.1407 - val_mae: 0.3116\n",
            "Epoch 19/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1323 - mae: 0.3004 - val_loss: 0.1241 - val_mae: 0.2942\n",
            "Epoch 20/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1171 - mae: 0.2845 - val_loss: 0.1214 - val_mae: 0.2856\n",
            "Epoch 21/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1136 - mae: 0.2758 - val_loss: 0.1154 - val_mae: 0.2786\n",
            "Epoch 22/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1093 - mae: 0.2703 - val_loss: 0.1109 - val_mae: 0.2662\n",
            "Epoch 23/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1065 - mae: 0.2595 - val_loss: 0.1081 - val_mae: 0.2625\n",
            "Epoch 24/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1022 - mae: 0.2519 - val_loss: 0.1073 - val_mae: 0.2552\n",
            "Epoch 25/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.1006 - mae: 0.2471 - val_loss: 0.1018 - val_mae: 0.2494\n",
            "Epoch 26/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0976 - mae: 0.2415 - val_loss: 0.0996 - val_mae: 0.2456\n",
            "Epoch 27/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0943 - mae: 0.2359 - val_loss: 0.0967 - val_mae: 0.2368\n",
            "Epoch 28/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0924 - mae: 0.2305 - val_loss: 0.0948 - val_mae: 0.2319\n",
            "Epoch 29/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0908 - mae: 0.2267 - val_loss: 0.0937 - val_mae: 0.2315\n",
            "Epoch 30/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0882 - mae: 0.2236 - val_loss: 0.0916 - val_mae: 0.2258\n",
            "Epoch 31/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0864 - mae: 0.2202 - val_loss: 0.0886 - val_mae: 0.2195\n",
            "Epoch 32/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0846 - mae: 0.2173 - val_loss: 0.0870 - val_mae: 0.2157\n",
            "Epoch 33/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0828 - mae: 0.2143 - val_loss: 0.0857 - val_mae: 0.2131\n",
            "Epoch 34/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0815 - mae: 0.2123 - val_loss: 0.0839 - val_mae: 0.2080\n",
            "Epoch 35/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0799 - mae: 0.2073 - val_loss: 0.0842 - val_mae: 0.2130\n",
            "Epoch 36/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0782 - mae: 0.2072 - val_loss: 0.0824 - val_mae: 0.2071\n",
            "Epoch 37/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0770 - mae: 0.2037 - val_loss: 0.0794 - val_mae: 0.2001\n",
            "Epoch 38/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0753 - mae: 0.1993 - val_loss: 0.0786 - val_mae: 0.2004\n",
            "Epoch 39/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0745 - mae: 0.1992 - val_loss: 0.0778 - val_mae: 0.1992\n",
            "Epoch 40/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0731 - mae: 0.1967 - val_loss: 0.0766 - val_mae: 0.1980\n",
            "Epoch 41/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0718 - mae: 0.1966 - val_loss: 0.0751 - val_mae: 0.1948\n",
            "Epoch 42/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0704 - mae: 0.1928 - val_loss: 0.0749 - val_mae: 0.1965\n",
            "Epoch 43/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0695 - mae: 0.1921 - val_loss: 0.0731 - val_mae: 0.1929\n",
            "Epoch 44/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0683 - mae: 0.1901 - val_loss: 0.0730 - val_mae: 0.1943\n",
            "Epoch 45/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0671 - mae: 0.1880 - val_loss: 0.0709 - val_mae: 0.1862\n",
            "Epoch 46/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0668 - mae: 0.1859 - val_loss: 0.0697 - val_mae: 0.1847\n",
            "Epoch 47/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0652 - mae: 0.1845 - val_loss: 0.0687 - val_mae: 0.1831\n",
            "Epoch 48/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0642 - mae: 0.1835 - val_loss: 0.0677 - val_mae: 0.1816\n",
            "Epoch 49/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0631 - mae: 0.1809 - val_loss: 0.0672 - val_mae: 0.1844\n",
            "Epoch 50/600\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.0627 - mae: 0.1811 - val_loss: 0.0663 - val_mae: 0.1829\n",
            "Epoch 51/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0610 - mae: 0.1803 - val_loss: 0.0654 - val_mae: 0.1814\n",
            "Epoch 52/600\n",
            "38/38 [==============================] - 0s 9ms/step - loss: 0.0600 - mae: 0.1765 - val_loss: 0.0648 - val_mae: 0.1804\n",
            "Epoch 53/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0585 - mae: 0.1777 - val_loss: 0.0625 - val_mae: 0.1715\n",
            "Epoch 54/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0585 - mae: 0.1730 - val_loss: 0.0625 - val_mae: 0.1768\n",
            "Epoch 55/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0567 - mae: 0.1725 - val_loss: 0.0633 - val_mae: 0.1845\n",
            "Epoch 56/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0560 - mae: 0.1710 - val_loss: 0.0622 - val_mae: 0.1800\n",
            "Epoch 57/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0547 - mae: 0.1704 - val_loss: 0.0588 - val_mae: 0.1730\n",
            "Epoch 58/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0534 - mae: 0.1667 - val_loss: 0.0579 - val_mae: 0.1709\n",
            "Epoch 59/600\n",
            "38/38 [==============================] - 0s 8ms/step - loss: 0.0531 - mae: 0.1686 - val_loss: 0.0564 - val_mae: 0.1697\n",
            "Epoch 60/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0514 - mae: 0.1651 - val_loss: 0.0556 - val_mae: 0.1684\n",
            "Epoch 61/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0504 - mae: 0.1631 - val_loss: 0.0546 - val_mae: 0.1687\n",
            "Epoch 62/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0487 - mae: 0.1614 - val_loss: 0.0535 - val_mae: 0.1608\n",
            "Epoch 63/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0480 - mae: 0.1609 - val_loss: 0.0519 - val_mae: 0.1599\n",
            "Epoch 64/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0472 - mae: 0.1564 - val_loss: 0.0550 - val_mae: 0.1788\n",
            "Epoch 65/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0462 - mae: 0.1578 - val_loss: 0.0492 - val_mae: 0.1579\n",
            "Epoch 66/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0449 - mae: 0.1541 - val_loss: 0.0473 - val_mae: 0.1503\n",
            "Epoch 67/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0444 - mae: 0.1529 - val_loss: 0.0471 - val_mae: 0.1554\n",
            "Epoch 68/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0434 - mae: 0.1520 - val_loss: 0.0464 - val_mae: 0.1566\n",
            "Epoch 69/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0418 - mae: 0.1500 - val_loss: 0.0444 - val_mae: 0.1478\n",
            "Epoch 70/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0415 - mae: 0.1497 - val_loss: 0.0436 - val_mae: 0.1490\n",
            "Epoch 71/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0399 - mae: 0.1462 - val_loss: 0.0472 - val_mae: 0.1575\n",
            "Epoch 72/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0391 - mae: 0.1455 - val_loss: 0.0437 - val_mae: 0.1552\n",
            "Epoch 73/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0383 - mae: 0.1430 - val_loss: 0.0443 - val_mae: 0.1527\n",
            "Epoch 74/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0368 - mae: 0.1426 - val_loss: 0.0399 - val_mae: 0.1424\n",
            "Epoch 75/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0360 - mae: 0.1401 - val_loss: 0.0411 - val_mae: 0.1536\n",
            "Epoch 76/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0348 - mae: 0.1384 - val_loss: 0.0395 - val_mae: 0.1475\n",
            "Epoch 77/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0343 - mae: 0.1384 - val_loss: 0.0368 - val_mae: 0.1389\n",
            "Epoch 78/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0330 - mae: 0.1354 - val_loss: 0.0346 - val_mae: 0.1342\n",
            "Epoch 79/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0323 - mae: 0.1345 - val_loss: 0.0336 - val_mae: 0.1321\n",
            "Epoch 80/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0313 - mae: 0.1317 - val_loss: 0.0355 - val_mae: 0.1429\n",
            "Epoch 81/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0304 - mae: 0.1308 - val_loss: 0.0326 - val_mae: 0.1266\n",
            "Epoch 82/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0299 - mae: 0.1290 - val_loss: 0.0404 - val_mae: 0.1620\n",
            "Epoch 83/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0287 - mae: 0.1268 - val_loss: 0.0317 - val_mae: 0.1331\n",
            "Epoch 84/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0276 - mae: 0.1244 - val_loss: 0.0305 - val_mae: 0.1254\n",
            "Epoch 85/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0275 - mae: 0.1244 - val_loss: 0.0283 - val_mae: 0.1233\n",
            "Epoch 86/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0269 - mae: 0.1242 - val_loss: 0.0335 - val_mae: 0.1357\n",
            "Epoch 87/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0268 - mae: 0.1243 - val_loss: 0.0271 - val_mae: 0.1202\n",
            "Epoch 88/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0256 - mae: 0.1213 - val_loss: 0.0297 - val_mae: 0.1324\n",
            "Epoch 89/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0250 - mae: 0.1196 - val_loss: 0.0264 - val_mae: 0.1182\n",
            "Epoch 90/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0246 - mae: 0.1188 - val_loss: 0.0261 - val_mae: 0.1208\n",
            "Epoch 91/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0238 - mae: 0.1176 - val_loss: 0.0242 - val_mae: 0.1151\n",
            "Epoch 92/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0235 - mae: 0.1168 - val_loss: 0.0249 - val_mae: 0.1170\n",
            "Epoch 93/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0231 - mae: 0.1160 - val_loss: 0.0248 - val_mae: 0.1177\n",
            "Epoch 94/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0216 - mae: 0.1127 - val_loss: 0.0242 - val_mae: 0.1158\n",
            "Epoch 95/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0222 - mae: 0.1131 - val_loss: 0.0224 - val_mae: 0.1116\n",
            "Epoch 96/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0218 - mae: 0.1142 - val_loss: 0.0248 - val_mae: 0.1225\n",
            "Epoch 97/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0210 - mae: 0.1115 - val_loss: 0.0257 - val_mae: 0.1169\n",
            "Epoch 98/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0212 - mae: 0.1107 - val_loss: 0.0212 - val_mae: 0.1103\n",
            "Epoch 99/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0206 - mae: 0.1085 - val_loss: 0.0207 - val_mae: 0.1083\n",
            "Epoch 100/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0205 - mae: 0.1103 - val_loss: 0.0202 - val_mae: 0.1070\n",
            "Epoch 101/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0200 - mae: 0.1089 - val_loss: 0.0204 - val_mae: 0.1099\n",
            "Epoch 102/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0196 - mae: 0.1083 - val_loss: 0.0208 - val_mae: 0.1097\n",
            "Epoch 103/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0194 - mae: 0.1075 - val_loss: 0.0224 - val_mae: 0.1097\n",
            "Epoch 104/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0199 - mae: 0.1074 - val_loss: 0.0225 - val_mae: 0.1194\n",
            "Epoch 105/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0187 - mae: 0.1065 - val_loss: 0.0192 - val_mae: 0.1058\n",
            "Epoch 106/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0188 - mae: 0.1063 - val_loss: 0.0183 - val_mae: 0.1030\n",
            "Epoch 107/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0190 - mae: 0.1054 - val_loss: 0.0192 - val_mae: 0.1076\n",
            "Epoch 108/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0183 - mae: 0.1052 - val_loss: 0.0202 - val_mae: 0.1077\n",
            "Epoch 109/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0184 - mae: 0.1050 - val_loss: 0.0177 - val_mae: 0.1026\n",
            "Epoch 110/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0176 - mae: 0.1032 - val_loss: 0.0195 - val_mae: 0.1105\n",
            "Epoch 111/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0177 - mae: 0.1034 - val_loss: 0.0198 - val_mae: 0.1084\n",
            "Epoch 112/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0178 - mae: 0.1029 - val_loss: 0.0206 - val_mae: 0.1136\n",
            "Epoch 113/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0174 - mae: 0.1038 - val_loss: 0.0168 - val_mae: 0.0985\n",
            "Epoch 114/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0171 - mae: 0.1025 - val_loss: 0.0165 - val_mae: 0.0996\n",
            "Epoch 115/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0171 - mae: 0.1014 - val_loss: 0.0157 - val_mae: 0.0962\n",
            "Epoch 116/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0169 - mae: 0.1019 - val_loss: 0.0182 - val_mae: 0.1054\n",
            "Epoch 117/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0166 - mae: 0.1012 - val_loss: 0.0179 - val_mae: 0.1037\n",
            "Epoch 118/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0159 - mae: 0.0982 - val_loss: 0.0158 - val_mae: 0.0967\n",
            "Epoch 119/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0164 - mae: 0.0998 - val_loss: 0.0170 - val_mae: 0.1020\n",
            "Epoch 120/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0158 - mae: 0.0980 - val_loss: 0.0159 - val_mae: 0.0978\n",
            "Epoch 121/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0160 - mae: 0.0996 - val_loss: 0.0160 - val_mae: 0.0989\n",
            "Epoch 122/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0159 - mae: 0.0975 - val_loss: 0.0211 - val_mae: 0.1156\n",
            "Epoch 123/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0159 - mae: 0.0984 - val_loss: 0.0207 - val_mae: 0.1097\n",
            "Epoch 124/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0157 - mae: 0.0993 - val_loss: 0.0178 - val_mae: 0.1048\n",
            "Epoch 125/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0163 - mae: 0.0998 - val_loss: 0.0152 - val_mae: 0.0958\n",
            "Epoch 126/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0152 - mae: 0.0967 - val_loss: 0.0152 - val_mae: 0.0955\n",
            "Epoch 127/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0154 - mae: 0.0977 - val_loss: 0.0156 - val_mae: 0.0977\n",
            "Epoch 128/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0153 - mae: 0.0983 - val_loss: 0.0167 - val_mae: 0.0997\n",
            "Epoch 129/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0158 - mae: 0.0993 - val_loss: 0.0140 - val_mae: 0.0925\n",
            "Epoch 130/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0151 - mae: 0.0972 - val_loss: 0.0172 - val_mae: 0.1025\n",
            "Epoch 131/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0148 - mae: 0.0948 - val_loss: 0.0165 - val_mae: 0.1017\n",
            "Epoch 132/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0150 - mae: 0.0964 - val_loss: 0.0175 - val_mae: 0.1062\n",
            "Epoch 133/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0152 - mae: 0.0977 - val_loss: 0.0141 - val_mae: 0.0934\n",
            "Epoch 134/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0151 - mae: 0.0963 - val_loss: 0.0148 - val_mae: 0.0935\n",
            "Epoch 135/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0151 - mae: 0.0961 - val_loss: 0.0143 - val_mae: 0.0941\n",
            "Epoch 136/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0148 - mae: 0.0942 - val_loss: 0.0158 - val_mae: 0.0997\n",
            "Epoch 137/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0150 - mae: 0.0969 - val_loss: 0.0147 - val_mae: 0.0944\n",
            "Epoch 138/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0144 - mae: 0.0946 - val_loss: 0.0136 - val_mae: 0.0920\n",
            "Epoch 139/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0145 - mae: 0.0950 - val_loss: 0.0137 - val_mae: 0.0919\n",
            "Epoch 140/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0146 - mae: 0.0945 - val_loss: 0.0130 - val_mae: 0.0902\n",
            "Epoch 141/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0145 - mae: 0.0949 - val_loss: 0.0141 - val_mae: 0.0928\n",
            "Epoch 142/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0142 - mae: 0.0944 - val_loss: 0.0128 - val_mae: 0.0895\n",
            "Epoch 143/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0144 - mae: 0.0944 - val_loss: 0.0196 - val_mae: 0.1152\n",
            "Epoch 144/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0147 - mae: 0.0956 - val_loss: 0.0149 - val_mae: 0.0977\n",
            "Epoch 145/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0146 - mae: 0.0949 - val_loss: 0.0151 - val_mae: 0.0964\n",
            "Epoch 146/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0144 - mae: 0.0947 - val_loss: 0.0209 - val_mae: 0.1164\n",
            "Epoch 147/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0146 - mae: 0.0947 - val_loss: 0.0134 - val_mae: 0.0919\n",
            "Epoch 148/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0140 - mae: 0.0942 - val_loss: 0.0141 - val_mae: 0.0941\n",
            "Epoch 149/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0143 - mae: 0.0945 - val_loss: 0.0140 - val_mae: 0.0933\n",
            "Epoch 150/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0145 - mae: 0.0945 - val_loss: 0.0151 - val_mae: 0.0973\n",
            "Epoch 151/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0915 - val_loss: 0.0142 - val_mae: 0.0946\n",
            "Epoch 152/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0141 - mae: 0.0938 - val_loss: 0.0127 - val_mae: 0.0892\n",
            "Epoch 153/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0141 - mae: 0.0937 - val_loss: 0.0174 - val_mae: 0.1068\n",
            "Epoch 154/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0140 - mae: 0.0935 - val_loss: 0.0138 - val_mae: 0.0935\n",
            "Epoch 155/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0141 - mae: 0.0933 - val_loss: 0.0138 - val_mae: 0.0936\n",
            "Epoch 156/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0141 - mae: 0.0937 - val_loss: 0.0140 - val_mae: 0.0955\n",
            "Epoch 157/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0138 - mae: 0.0930 - val_loss: 0.0146 - val_mae: 0.0944\n",
            "Epoch 158/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0142 - mae: 0.0938 - val_loss: 0.0129 - val_mae: 0.0906\n",
            "Epoch 159/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0137 - mae: 0.0932 - val_loss: 0.0159 - val_mae: 0.1014\n",
            "Epoch 160/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0144 - mae: 0.0940 - val_loss: 0.0128 - val_mae: 0.0901\n",
            "Epoch 161/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0132 - mae: 0.0911 - val_loss: 0.0153 - val_mae: 0.1009\n",
            "Epoch 162/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0139 - mae: 0.0932 - val_loss: 0.0149 - val_mae: 0.0952\n",
            "Epoch 163/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0138 - mae: 0.0935 - val_loss: 0.0135 - val_mae: 0.0924\n",
            "Epoch 164/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0136 - mae: 0.0926 - val_loss: 0.0248 - val_mae: 0.1318\n",
            "Epoch 165/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0142 - mae: 0.0950 - val_loss: 0.0133 - val_mae: 0.0923\n",
            "Epoch 166/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0134 - mae: 0.0918 - val_loss: 0.0131 - val_mae: 0.0914\n",
            "Epoch 167/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0138 - mae: 0.0929 - val_loss: 0.0144 - val_mae: 0.0968\n",
            "Epoch 168/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0134 - mae: 0.0919 - val_loss: 0.0146 - val_mae: 0.0948\n",
            "Epoch 169/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0135 - mae: 0.0913 - val_loss: 0.0146 - val_mae: 0.0963\n",
            "Epoch 170/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0136 - mae: 0.0926 - val_loss: 0.0125 - val_mae: 0.0893\n",
            "Epoch 171/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0920 - val_loss: 0.0128 - val_mae: 0.0896\n",
            "Epoch 172/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0139 - mae: 0.0928 - val_loss: 0.0140 - val_mae: 0.0934\n",
            "Epoch 173/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0920 - val_loss: 0.0122 - val_mae: 0.0883\n",
            "Epoch 174/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0913 - val_loss: 0.0132 - val_mae: 0.0913\n",
            "Epoch 175/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0937 - val_loss: 0.0130 - val_mae: 0.0915\n",
            "Epoch 176/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0133 - mae: 0.0910 - val_loss: 0.0129 - val_mae: 0.0907\n",
            "Epoch 177/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0134 - mae: 0.0908 - val_loss: 0.0131 - val_mae: 0.0908\n",
            "Epoch 178/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0919 - val_loss: 0.0119 - val_mae: 0.0869\n",
            "Epoch 179/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0137 - mae: 0.0936 - val_loss: 0.0125 - val_mae: 0.0893\n",
            "Epoch 180/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0132 - mae: 0.0906 - val_loss: 0.0155 - val_mae: 0.0996\n",
            "Epoch 181/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0132 - mae: 0.0906 - val_loss: 0.0138 - val_mae: 0.0936\n",
            "Epoch 182/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0916 - val_loss: 0.0143 - val_mae: 0.0958\n",
            "Epoch 183/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0132 - mae: 0.0921 - val_loss: 0.0138 - val_mae: 0.0942\n",
            "Epoch 184/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0134 - mae: 0.0917 - val_loss: 0.0120 - val_mae: 0.0870\n",
            "Epoch 185/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0132 - mae: 0.0928 - val_loss: 0.0134 - val_mae: 0.0935\n",
            "Epoch 186/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0906 - val_loss: 0.0124 - val_mae: 0.0886\n",
            "Epoch 187/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0901 - val_loss: 0.0155 - val_mae: 0.0975\n",
            "Epoch 188/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0134 - mae: 0.0915 - val_loss: 0.0123 - val_mae: 0.0885\n",
            "Epoch 189/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0898 - val_loss: 0.0136 - val_mae: 0.0944\n",
            "Epoch 190/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0912 - val_loss: 0.0117 - val_mae: 0.0861\n",
            "Epoch 191/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0135 - mae: 0.0921 - val_loss: 0.0117 - val_mae: 0.0864\n",
            "Epoch 192/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0133 - mae: 0.0914 - val_loss: 0.0164 - val_mae: 0.1016\n",
            "Epoch 193/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0924 - val_loss: 0.0155 - val_mae: 0.1003\n",
            "Epoch 194/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0913 - val_loss: 0.0123 - val_mae: 0.0885\n",
            "Epoch 195/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0135 - mae: 0.0929 - val_loss: 0.0136 - val_mae: 0.0933\n",
            "Epoch 196/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0881 - val_loss: 0.0141 - val_mae: 0.0962\n",
            "Epoch 197/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0927 - val_loss: 0.0116 - val_mae: 0.0859\n",
            "Epoch 198/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0129 - mae: 0.0909 - val_loss: 0.0120 - val_mae: 0.0882\n",
            "Epoch 199/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0894 - val_loss: 0.0123 - val_mae: 0.0890\n",
            "Epoch 200/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0899 - val_loss: 0.0128 - val_mae: 0.0907\n",
            "Epoch 201/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0908 - val_loss: 0.0161 - val_mae: 0.0989\n",
            "Epoch 202/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0132 - mae: 0.0910 - val_loss: 0.0152 - val_mae: 0.0989\n",
            "Epoch 203/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0895 - val_loss: 0.0120 - val_mae: 0.0875\n",
            "Epoch 204/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0877 - val_loss: 0.0175 - val_mae: 0.1067\n",
            "Epoch 205/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0131 - mae: 0.0912 - val_loss: 0.0134 - val_mae: 0.0920\n",
            "Epoch 206/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0883 - val_loss: 0.0133 - val_mae: 0.0925\n",
            "Epoch 207/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0911 - val_loss: 0.0137 - val_mae: 0.0934\n",
            "Epoch 208/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0911 - val_loss: 0.0118 - val_mae: 0.0871\n",
            "Epoch 209/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0911 - val_loss: 0.0199 - val_mae: 0.1111\n",
            "Epoch 210/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0915 - val_loss: 0.0117 - val_mae: 0.0869\n",
            "Epoch 211/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0892 - val_loss: 0.0136 - val_mae: 0.0929\n",
            "Epoch 212/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0886 - val_loss: 0.0132 - val_mae: 0.0917\n",
            "Epoch 213/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0890 - val_loss: 0.0126 - val_mae: 0.0898\n",
            "Epoch 214/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0910 - val_loss: 0.0131 - val_mae: 0.0917\n",
            "Epoch 215/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0902 - val_loss: 0.0117 - val_mae: 0.0867\n",
            "Epoch 216/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0902 - val_loss: 0.0152 - val_mae: 0.0992\n",
            "Epoch 217/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0893 - val_loss: 0.0137 - val_mae: 0.0931\n",
            "Epoch 218/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0915 - val_loss: 0.0116 - val_mae: 0.0865\n",
            "Epoch 219/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0901 - val_loss: 0.0128 - val_mae: 0.0909\n",
            "Epoch 220/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0895 - val_loss: 0.0118 - val_mae: 0.0868\n",
            "Epoch 221/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0877 - val_loss: 0.0140 - val_mae: 0.0960\n",
            "Epoch 222/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0881 - val_loss: 0.0141 - val_mae: 0.0943\n",
            "Epoch 223/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0891 - val_loss: 0.0140 - val_mae: 0.0957\n",
            "Epoch 224/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0896 - val_loss: 0.0119 - val_mae: 0.0867\n",
            "Epoch 225/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0882 - val_loss: 0.0121 - val_mae: 0.0887\n",
            "Epoch 226/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0130 - mae: 0.0901 - val_loss: 0.0142 - val_mae: 0.0982\n",
            "Epoch 227/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0128 - mae: 0.0905 - val_loss: 0.0112 - val_mae: 0.0849\n",
            "Epoch 228/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0879 - val_loss: 0.0202 - val_mae: 0.1169\n",
            "Epoch 229/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.0894 - val_loss: 0.0138 - val_mae: 0.0941\n",
            "Epoch 230/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0895 - val_loss: 0.0131 - val_mae: 0.0912\n",
            "Epoch 231/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0891 - val_loss: 0.0113 - val_mae: 0.0855\n",
            "Epoch 232/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0131 - mae: 0.0924 - val_loss: 0.0130 - val_mae: 0.0914\n",
            "Epoch 233/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0888 - val_loss: 0.0146 - val_mae: 0.0975\n",
            "Epoch 234/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0128 - mae: 0.0908 - val_loss: 0.0145 - val_mae: 0.0983\n",
            "Epoch 235/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.0904 - val_loss: 0.0123 - val_mae: 0.0891\n",
            "Epoch 236/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.0900 - val_loss: 0.0119 - val_mae: 0.0874\n",
            "Epoch 237/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0123 - mae: 0.0879 - val_loss: 0.0120 - val_mae: 0.0881\n",
            "Epoch 238/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0893 - val_loss: 0.0142 - val_mae: 0.0954\n",
            "Epoch 239/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0129 - mae: 0.0901 - val_loss: 0.0114 - val_mae: 0.0859\n",
            "Epoch 240/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0883 - val_loss: 0.0117 - val_mae: 0.0869\n",
            "Epoch 241/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0130 - mae: 0.0915 - val_loss: 0.0121 - val_mae: 0.0882\n",
            "Epoch 242/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0875 - val_loss: 0.0121 - val_mae: 0.0885\n",
            "Epoch 243/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0128 - mae: 0.0898 - val_loss: 0.0121 - val_mae: 0.0885\n",
            "Epoch 244/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0126 - mae: 0.0903 - val_loss: 0.0122 - val_mae: 0.0883\n",
            "Epoch 245/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0130 - mae: 0.0909 - val_loss: 0.0112 - val_mae: 0.0847\n",
            "Epoch 246/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0891 - val_loss: 0.0191 - val_mae: 0.1110\n",
            "Epoch 247/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0884 - val_loss: 0.0131 - val_mae: 0.0935\n",
            "Epoch 248/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0884 - val_loss: 0.0155 - val_mae: 0.0979\n",
            "Epoch 249/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0886 - val_loss: 0.0112 - val_mae: 0.0846\n",
            "Epoch 250/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0901 - val_loss: 0.0118 - val_mae: 0.0872\n",
            "Epoch 251/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0916 - val_loss: 0.0119 - val_mae: 0.0872\n",
            "Epoch 252/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0898 - val_loss: 0.0125 - val_mae: 0.0898\n",
            "Epoch 253/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0881 - val_loss: 0.0127 - val_mae: 0.0899\n",
            "Epoch 254/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0920 - val_loss: 0.0113 - val_mae: 0.0852\n",
            "Epoch 255/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0890 - val_loss: 0.0124 - val_mae: 0.0900\n",
            "Epoch 256/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0901 - val_loss: 0.0145 - val_mae: 0.0964\n",
            "Epoch 257/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0909 - val_loss: 0.0119 - val_mae: 0.0872\n",
            "Epoch 258/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0918 - val_loss: 0.0124 - val_mae: 0.0894\n",
            "Epoch 259/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0890 - val_loss: 0.0162 - val_mae: 0.1043\n",
            "Epoch 260/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0899 - val_loss: 0.0168 - val_mae: 0.1020\n",
            "Epoch 261/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0908 - val_loss: 0.0123 - val_mae: 0.0886\n",
            "Epoch 262/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0899 - val_loss: 0.0116 - val_mae: 0.0859\n",
            "Epoch 263/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0897 - val_loss: 0.0121 - val_mae: 0.0881\n",
            "Epoch 264/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0896 - val_loss: 0.0134 - val_mae: 0.0921\n",
            "Epoch 265/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0897 - val_loss: 0.0171 - val_mae: 0.1058\n",
            "Epoch 266/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0901 - val_loss: 0.0177 - val_mae: 0.1071\n",
            "Epoch 267/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0900 - val_loss: 0.0151 - val_mae: 0.0975\n",
            "Epoch 268/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0905 - val_loss: 0.0136 - val_mae: 0.0933\n",
            "Epoch 269/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0894 - val_loss: 0.0125 - val_mae: 0.0890\n",
            "Epoch 270/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0877 - val_loss: 0.0114 - val_mae: 0.0853\n",
            "Epoch 271/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0901 - val_loss: 0.0166 - val_mae: 0.1042\n",
            "Epoch 272/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0898 - val_loss: 0.0138 - val_mae: 0.0954\n",
            "Epoch 273/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0884 - val_loss: 0.0135 - val_mae: 0.0917\n",
            "Epoch 274/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0894 - val_loss: 0.0116 - val_mae: 0.0860\n",
            "Epoch 275/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0884 - val_loss: 0.0116 - val_mae: 0.0865\n",
            "Epoch 276/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0883 - val_loss: 0.0116 - val_mae: 0.0862\n",
            "Epoch 277/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0907 - val_loss: 0.0145 - val_mae: 0.0962\n",
            "Epoch 278/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0909 - val_loss: 0.0147 - val_mae: 0.0973\n",
            "Epoch 279/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0130 - mae: 0.0905 - val_loss: 0.0109 - val_mae: 0.0837\n",
            "Epoch 280/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0895 - val_loss: 0.0125 - val_mae: 0.0897\n",
            "Epoch 281/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0888 - val_loss: 0.0131 - val_mae: 0.0923\n",
            "Epoch 282/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0112 - val_mae: 0.0851\n",
            "Epoch 283/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0901 - val_loss: 0.0140 - val_mae: 0.0945\n",
            "Epoch 284/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0129 - mae: 0.0911 - val_loss: 0.0184 - val_mae: 0.1108\n",
            "Epoch 285/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0893 - val_loss: 0.0181 - val_mae: 0.1068\n",
            "Epoch 286/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.0904 - val_loss: 0.0115 - val_mae: 0.0865\n",
            "Epoch 287/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0894 - val_loss: 0.0147 - val_mae: 0.0968\n",
            "Epoch 288/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0132 - val_mae: 0.0923\n",
            "Epoch 289/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0894 - val_loss: 0.0128 - val_mae: 0.0910\n",
            "Epoch 290/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0891 - val_loss: 0.0183 - val_mae: 0.1103\n",
            "Epoch 291/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0882 - val_loss: 0.0160 - val_mae: 0.1014\n",
            "Epoch 292/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0131 - mae: 0.0918 - val_loss: 0.0112 - val_mae: 0.0851\n",
            "Epoch 293/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0900 - val_loss: 0.0152 - val_mae: 0.0981\n",
            "Epoch 294/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0894 - val_loss: 0.0130 - val_mae: 0.0913\n",
            "Epoch 295/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0906 - val_loss: 0.0111 - val_mae: 0.0846\n",
            "Epoch 296/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0127 - mae: 0.0915 - val_loss: 0.0119 - val_mae: 0.0873\n",
            "Epoch 297/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0118 - val_mae: 0.0874\n",
            "Epoch 298/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0114 - val_mae: 0.0857\n",
            "Epoch 299/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0899 - val_loss: 0.0174 - val_mae: 0.1066\n",
            "Epoch 300/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0119 - mae: 0.0875 - val_loss: 0.0152 - val_mae: 0.0972\n",
            "Epoch 301/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0887 - val_loss: 0.0150 - val_mae: 0.0972\n",
            "Epoch 302/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0118 - mae: 0.0876 - val_loss: 0.0146 - val_mae: 0.0964\n",
            "Epoch 303/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0875 - val_loss: 0.0116 - val_mae: 0.0863\n",
            "Epoch 304/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0887 - val_loss: 0.0138 - val_mae: 0.0944\n",
            "Epoch 305/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0128 - mae: 0.0908 - val_loss: 0.0113 - val_mae: 0.0849\n",
            "Epoch 306/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0881 - val_loss: 0.0140 - val_mae: 0.0952\n",
            "Epoch 307/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0896 - val_loss: 0.0111 - val_mae: 0.0842\n",
            "Epoch 308/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0879 - val_loss: 0.0122 - val_mae: 0.0885\n",
            "Epoch 309/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0881 - val_loss: 0.0115 - val_mae: 0.0866\n",
            "Epoch 310/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0890 - val_loss: 0.0133 - val_mae: 0.0918\n",
            "Epoch 311/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0880 - val_loss: 0.0134 - val_mae: 0.0928\n",
            "Epoch 312/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0868 - val_loss: 0.0141 - val_mae: 0.0958\n",
            "Epoch 313/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0906 - val_loss: 0.0145 - val_mae: 0.0973\n",
            "Epoch 314/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0882 - val_loss: 0.0142 - val_mae: 0.0956\n",
            "Epoch 315/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0890 - val_loss: 0.0110 - val_mae: 0.0839\n",
            "Epoch 316/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0893 - val_loss: 0.0116 - val_mae: 0.0860\n",
            "Epoch 317/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0886 - val_loss: 0.0137 - val_mae: 0.0943\n",
            "Epoch 318/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0127 - mae: 0.0920 - val_loss: 0.0155 - val_mae: 0.0998\n",
            "Epoch 319/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0119 - mae: 0.0865 - val_loss: 0.0146 - val_mae: 0.0952\n",
            "Epoch 320/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0121 - mae: 0.0875 - val_loss: 0.0128 - val_mae: 0.0902\n",
            "Epoch 321/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0890 - val_loss: 0.0116 - val_mae: 0.0864\n",
            "Epoch 322/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0133 - val_mae: 0.0933\n",
            "Epoch 323/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0127 - mae: 0.0904 - val_loss: 0.0112 - val_mae: 0.0843\n",
            "Epoch 324/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0898 - val_loss: 0.0160 - val_mae: 0.1026\n",
            "Epoch 325/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0894 - val_loss: 0.0167 - val_mae: 0.1020\n",
            "Epoch 326/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0124 - mae: 0.0895 - val_loss: 0.0117 - val_mae: 0.0870\n",
            "Epoch 327/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0126 - mae: 0.0907 - val_loss: 0.0129 - val_mae: 0.0914\n",
            "Epoch 328/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0128 - mae: 0.0904 - val_loss: 0.0112 - val_mae: 0.0848\n",
            "Epoch 329/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0126 - mae: 0.0902 - val_loss: 0.0137 - val_mae: 0.0935\n",
            "Epoch 330/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0120 - mae: 0.0880 - val_loss: 0.0185 - val_mae: 0.1100\n",
            "Epoch 331/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0130 - mae: 0.0917 - val_loss: 0.0135 - val_mae: 0.0927\n",
            "Epoch 332/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0123 - mae: 0.0888 - val_loss: 0.0149 - val_mae: 0.0978\n",
            "Epoch 333/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0895 - val_loss: 0.0122 - val_mae: 0.0879\n",
            "Epoch 334/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0882 - val_loss: 0.0142 - val_mae: 0.0952\n",
            "Epoch 335/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0881 - val_loss: 0.0115 - val_mae: 0.0858\n",
            "Epoch 336/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0124 - mae: 0.0895 - val_loss: 0.0112 - val_mae: 0.0852\n",
            "Epoch 337/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0885 - val_loss: 0.0144 - val_mae: 0.0971\n",
            "Epoch 338/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0902 - val_loss: 0.0125 - val_mae: 0.0893\n",
            "Epoch 339/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0120 - mae: 0.0878 - val_loss: 0.0138 - val_mae: 0.0945\n",
            "Epoch 340/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0888 - val_loss: 0.0128 - val_mae: 0.0907\n",
            "Epoch 341/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0114 - val_mae: 0.0854\n",
            "Epoch 342/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0893 - val_loss: 0.0196 - val_mae: 0.1148\n",
            "Epoch 343/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0906 - val_loss: 0.0123 - val_mae: 0.0891\n",
            "Epoch 344/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0890 - val_loss: 0.0134 - val_mae: 0.0939\n",
            "Epoch 345/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0879 - val_loss: 0.0136 - val_mae: 0.0929\n",
            "Epoch 346/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0901 - val_loss: 0.0124 - val_mae: 0.0892\n",
            "Epoch 347/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0887 - val_loss: 0.0138 - val_mae: 0.0932\n",
            "Epoch 348/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0892 - val_loss: 0.0127 - val_mae: 0.0893\n",
            "Epoch 349/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0121 - val_mae: 0.0887\n",
            "Epoch 350/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0900 - val_loss: 0.0121 - val_mae: 0.0882\n",
            "Epoch 351/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0874 - val_loss: 0.0163 - val_mae: 0.1047\n",
            "Epoch 352/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0127 - mae: 0.0898 - val_loss: 0.0139 - val_mae: 0.0961\n",
            "Epoch 353/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0884 - val_loss: 0.0123 - val_mae: 0.0893\n",
            "Epoch 354/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0907 - val_loss: 0.0152 - val_mae: 0.0993\n",
            "Epoch 355/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0901 - val_loss: 0.0134 - val_mae: 0.0916\n",
            "Epoch 356/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0891 - val_loss: 0.0123 - val_mae: 0.0883\n",
            "Epoch 357/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0887 - val_loss: 0.0109 - val_mae: 0.0836\n",
            "Epoch 358/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0898 - val_loss: 0.0116 - val_mae: 0.0856\n",
            "Epoch 359/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0110 - val_mae: 0.0840\n",
            "Epoch 360/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0883 - val_loss: 0.0149 - val_mae: 0.0983\n",
            "Epoch 361/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0905 - val_loss: 0.0127 - val_mae: 0.0896\n",
            "Epoch 362/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0896 - val_loss: 0.0120 - val_mae: 0.0875\n",
            "Epoch 363/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0897 - val_loss: 0.0140 - val_mae: 0.0962\n",
            "Epoch 364/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0899 - val_loss: 0.0144 - val_mae: 0.0954\n",
            "Epoch 365/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0893 - val_loss: 0.0137 - val_mae: 0.0943\n",
            "Epoch 366/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0899 - val_loss: 0.0119 - val_mae: 0.0875\n",
            "Epoch 367/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0916 - val_loss: 0.0130 - val_mae: 0.0913\n",
            "Epoch 368/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0881 - val_loss: 0.0112 - val_mae: 0.0850\n",
            "Epoch 369/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0875 - val_loss: 0.0108 - val_mae: 0.0830\n",
            "Epoch 370/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0870 - val_loss: 0.0180 - val_mae: 0.1039\n",
            "Epoch 371/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0887 - val_loss: 0.0121 - val_mae: 0.0886\n",
            "Epoch 372/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0114 - val_mae: 0.0855\n",
            "Epoch 373/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0899 - val_loss: 0.0165 - val_mae: 0.1021\n",
            "Epoch 374/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0897 - val_loss: 0.0123 - val_mae: 0.0885\n",
            "Epoch 375/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0891 - val_loss: 0.0118 - val_mae: 0.0879\n",
            "Epoch 376/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0889 - val_loss: 0.0111 - val_mae: 0.0841\n",
            "Epoch 377/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0893 - val_loss: 0.0136 - val_mae: 0.0948\n",
            "Epoch 378/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0889 - val_loss: 0.0114 - val_mae: 0.0854\n",
            "Epoch 379/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0885 - val_loss: 0.0142 - val_mae: 0.0967\n",
            "Epoch 380/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0891 - val_loss: 0.0128 - val_mae: 0.0907\n",
            "Epoch 381/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0865 - val_loss: 0.0120 - val_mae: 0.0870\n",
            "Epoch 382/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0876 - val_loss: 0.0212 - val_mae: 0.1199\n",
            "Epoch 383/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0119 - val_mae: 0.0878\n",
            "Epoch 384/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0888 - val_loss: 0.0175 - val_mae: 0.1069\n",
            "Epoch 385/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0127 - mae: 0.0906 - val_loss: 0.0126 - val_mae: 0.0901\n",
            "Epoch 386/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0865 - val_loss: 0.0111 - val_mae: 0.0843\n",
            "Epoch 387/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0896 - val_loss: 0.0183 - val_mae: 0.1082\n",
            "Epoch 388/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0128 - mae: 0.0909 - val_loss: 0.0115 - val_mae: 0.0858\n",
            "Epoch 389/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0883 - val_loss: 0.0126 - val_mae: 0.0889\n",
            "Epoch 390/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0127 - mae: 0.0910 - val_loss: 0.0141 - val_mae: 0.0959\n",
            "Epoch 391/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0881 - val_loss: 0.0112 - val_mae: 0.0845\n",
            "Epoch 392/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0882 - val_loss: 0.0215 - val_mae: 0.1168\n",
            "Epoch 393/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0899 - val_loss: 0.0164 - val_mae: 0.1036\n",
            "Epoch 394/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0903 - val_loss: 0.0136 - val_mae: 0.0931\n",
            "Epoch 395/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0893 - val_loss: 0.0118 - val_mae: 0.0871\n",
            "Epoch 396/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0893 - val_loss: 0.0141 - val_mae: 0.0944\n",
            "Epoch 397/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0120 - mae: 0.0881 - val_loss: 0.0121 - val_mae: 0.0885\n",
            "Epoch 398/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0891 - val_loss: 0.0117 - val_mae: 0.0867\n",
            "Epoch 399/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0142 - val_mae: 0.0968\n",
            "Epoch 400/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0119 - mae: 0.0880 - val_loss: 0.0156 - val_mae: 0.1004\n",
            "Epoch 401/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0125 - mae: 0.0909 - val_loss: 0.0139 - val_mae: 0.0931\n",
            "Epoch 402/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0895 - val_loss: 0.0117 - val_mae: 0.0869\n",
            "Epoch 403/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0879 - val_loss: 0.0117 - val_mae: 0.0863\n",
            "Epoch 404/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0897 - val_loss: 0.0144 - val_mae: 0.0949\n",
            "Epoch 405/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0119 - mae: 0.0873 - val_loss: 0.0112 - val_mae: 0.0847\n",
            "Epoch 406/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0883 - val_loss: 0.0113 - val_mae: 0.0849\n",
            "Epoch 407/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0124 - mae: 0.0898 - val_loss: 0.0147 - val_mae: 0.0971\n",
            "Epoch 408/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0893 - val_loss: 0.0119 - val_mae: 0.0873\n",
            "Epoch 409/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0119 - mae: 0.0880 - val_loss: 0.0146 - val_mae: 0.0975\n",
            "Epoch 410/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0118 - mae: 0.0872 - val_loss: 0.0150 - val_mae: 0.0972\n",
            "Epoch 411/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0125 - mae: 0.0889 - val_loss: 0.0114 - val_mae: 0.0855\n",
            "Epoch 412/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0890 - val_loss: 0.0126 - val_mae: 0.0905\n",
            "Epoch 413/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0886 - val_loss: 0.0111 - val_mae: 0.0846\n",
            "Epoch 414/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0121 - mae: 0.0878 - val_loss: 0.0107 - val_mae: 0.0830\n",
            "Epoch 415/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0889 - val_loss: 0.0138 - val_mae: 0.0943\n",
            "Epoch 416/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0123 - mae: 0.0886 - val_loss: 0.0115 - val_mae: 0.0860\n",
            "Epoch 417/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0892 - val_loss: 0.0196 - val_mae: 0.1107\n",
            "Epoch 418/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0881 - val_loss: 0.0165 - val_mae: 0.1051\n",
            "Epoch 419/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0124 - mae: 0.0899 - val_loss: 0.0116 - val_mae: 0.0859\n",
            "Epoch 420/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0878 - val_loss: 0.0127 - val_mae: 0.0908\n",
            "Epoch 421/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0127 - mae: 0.0895 - val_loss: 0.0109 - val_mae: 0.0833\n",
            "Epoch 422/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0881 - val_loss: 0.0129 - val_mae: 0.0908\n",
            "Epoch 423/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0889 - val_loss: 0.0124 - val_mae: 0.0893\n",
            "Epoch 424/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0893 - val_loss: 0.0123 - val_mae: 0.0887\n",
            "Epoch 425/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0118 - mae: 0.0876 - val_loss: 0.0116 - val_mae: 0.0859\n",
            "Epoch 426/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0891 - val_loss: 0.0111 - val_mae: 0.0842\n",
            "Epoch 427/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0879 - val_loss: 0.0108 - val_mae: 0.0830\n",
            "Epoch 428/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0897 - val_loss: 0.0199 - val_mae: 0.1130\n",
            "Epoch 429/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0897 - val_loss: 0.0119 - val_mae: 0.0870\n",
            "Epoch 430/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0899 - val_loss: 0.0115 - val_mae: 0.0861\n",
            "Epoch 431/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0903 - val_loss: 0.0113 - val_mae: 0.0851\n",
            "Epoch 432/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0899 - val_loss: 0.0112 - val_mae: 0.0853\n",
            "Epoch 433/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0891 - val_loss: 0.0160 - val_mae: 0.1002\n",
            "Epoch 434/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0907 - val_loss: 0.0115 - val_mae: 0.0858\n",
            "Epoch 435/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0879 - val_loss: 0.0126 - val_mae: 0.0895\n",
            "Epoch 436/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0119 - mae: 0.0873 - val_loss: 0.0108 - val_mae: 0.0829\n",
            "Epoch 437/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0887 - val_loss: 0.0114 - val_mae: 0.0851\n",
            "Epoch 438/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0894 - val_loss: 0.0115 - val_mae: 0.0858\n",
            "Epoch 439/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0892 - val_loss: 0.0184 - val_mae: 0.1110\n",
            "Epoch 440/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0901 - val_loss: 0.0115 - val_mae: 0.0857\n",
            "Epoch 441/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0874 - val_loss: 0.0114 - val_mae: 0.0855\n",
            "Epoch 442/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0895 - val_loss: 0.0112 - val_mae: 0.0848\n",
            "Epoch 443/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0141 - val_mae: 0.0955\n",
            "Epoch 444/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0890 - val_loss: 0.0166 - val_mae: 0.1024\n",
            "Epoch 445/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0117 - mae: 0.0863 - val_loss: 0.0127 - val_mae: 0.0900\n",
            "Epoch 446/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0896 - val_loss: 0.0112 - val_mae: 0.0846\n",
            "Epoch 447/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0118 - mae: 0.0868 - val_loss: 0.0175 - val_mae: 0.1086\n",
            "Epoch 448/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0910 - val_loss: 0.0123 - val_mae: 0.0884\n",
            "Epoch 449/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0126 - mae: 0.0894 - val_loss: 0.0147 - val_mae: 0.0967\n",
            "Epoch 450/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0118 - mae: 0.0868 - val_loss: 0.0171 - val_mae: 0.1065\n",
            "Epoch 451/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0888 - val_loss: 0.0114 - val_mae: 0.0851\n",
            "Epoch 452/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0889 - val_loss: 0.0125 - val_mae: 0.0891\n",
            "Epoch 453/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0884 - val_loss: 0.0143 - val_mae: 0.0952\n",
            "Epoch 454/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0128 - mae: 0.0912 - val_loss: 0.0128 - val_mae: 0.0896\n",
            "Epoch 455/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0902 - val_loss: 0.0111 - val_mae: 0.0838\n",
            "Epoch 456/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0869 - val_loss: 0.0133 - val_mae: 0.0919\n",
            "Epoch 457/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0886 - val_loss: 0.0142 - val_mae: 0.0952\n",
            "Epoch 458/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0156 - val_mae: 0.1013\n",
            "Epoch 459/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0892 - val_loss: 0.0109 - val_mae: 0.0839\n",
            "Epoch 460/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0117 - mae: 0.0866 - val_loss: 0.0127 - val_mae: 0.0895\n",
            "Epoch 461/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0883 - val_loss: 0.0109 - val_mae: 0.0833\n",
            "Epoch 462/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0888 - val_loss: 0.0117 - val_mae: 0.0866\n",
            "Epoch 463/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0886 - val_loss: 0.0126 - val_mae: 0.0896\n",
            "Epoch 464/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0180 - val_mae: 0.1069\n",
            "Epoch 465/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0878 - val_loss: 0.0146 - val_mae: 0.0964\n",
            "Epoch 466/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0899 - val_loss: 0.0127 - val_mae: 0.0899\n",
            "Epoch 467/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0117 - mae: 0.0873 - val_loss: 0.0158 - val_mae: 0.0996\n",
            "Epoch 468/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0896 - val_loss: 0.0107 - val_mae: 0.0828\n",
            "Epoch 469/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0883 - val_loss: 0.0119 - val_mae: 0.0872\n",
            "Epoch 470/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0882 - val_loss: 0.0114 - val_mae: 0.0861\n",
            "Epoch 471/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0125 - mae: 0.0900 - val_loss: 0.0124 - val_mae: 0.0889\n",
            "Epoch 472/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0886 - val_loss: 0.0138 - val_mae: 0.0933\n",
            "Epoch 473/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0886 - val_loss: 0.0152 - val_mae: 0.0971\n",
            "Epoch 474/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0917 - val_loss: 0.0118 - val_mae: 0.0874\n",
            "Epoch 475/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0889 - val_loss: 0.0111 - val_mae: 0.0842\n",
            "Epoch 476/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0889 - val_loss: 0.0131 - val_mae: 0.0916\n",
            "Epoch 477/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0896 - val_loss: 0.0122 - val_mae: 0.0880\n",
            "Epoch 478/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0886 - val_loss: 0.0111 - val_mae: 0.0839\n",
            "Epoch 479/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0875 - val_loss: 0.0120 - val_mae: 0.0869\n",
            "Epoch 480/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0889 - val_loss: 0.0107 - val_mae: 0.0828\n",
            "Epoch 481/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0886 - val_loss: 0.0194 - val_mae: 0.1149\n",
            "Epoch 482/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0870 - val_loss: 0.0152 - val_mae: 0.0997\n",
            "Epoch 483/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0898 - val_loss: 0.0115 - val_mae: 0.0857\n",
            "Epoch 484/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0874 - val_loss: 0.0112 - val_mae: 0.0847\n",
            "Epoch 485/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0884 - val_loss: 0.0112 - val_mae: 0.0848\n",
            "Epoch 486/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0888 - val_loss: 0.0160 - val_mae: 0.1013\n",
            "Epoch 487/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.0869 - val_loss: 0.0119 - val_mae: 0.0873\n",
            "Epoch 488/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0118 - mae: 0.0887 - val_loss: 0.0130 - val_mae: 0.0908\n",
            "Epoch 489/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0882 - val_loss: 0.0123 - val_mae: 0.0881\n",
            "Epoch 490/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0111 - val_mae: 0.0842\n",
            "Epoch 491/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0891 - val_loss: 0.0116 - val_mae: 0.0867\n",
            "Epoch 492/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0902 - val_loss: 0.0128 - val_mae: 0.0901\n",
            "Epoch 493/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0892 - val_loss: 0.0137 - val_mae: 0.0930\n",
            "Epoch 494/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0896 - val_loss: 0.0164 - val_mae: 0.0998\n",
            "Epoch 495/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0909 - val_loss: 0.0128 - val_mae: 0.0899\n",
            "Epoch 496/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0893 - val_loss: 0.0143 - val_mae: 0.0968\n",
            "Epoch 497/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0898 - val_loss: 0.0112 - val_mae: 0.0848\n",
            "Epoch 498/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0899 - val_loss: 0.0125 - val_mae: 0.0889\n",
            "Epoch 499/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0120 - mae: 0.0883 - val_loss: 0.0119 - val_mae: 0.0870\n",
            "Epoch 500/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0125 - mae: 0.0901 - val_loss: 0.0122 - val_mae: 0.0878\n",
            "Epoch 501/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0125 - mae: 0.0905 - val_loss: 0.0124 - val_mae: 0.0889\n",
            "Epoch 502/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0890 - val_loss: 0.0143 - val_mae: 0.0948\n",
            "Epoch 503/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0127 - mae: 0.0906 - val_loss: 0.0110 - val_mae: 0.0836\n",
            "Epoch 504/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0119 - mae: 0.0874 - val_loss: 0.0117 - val_mae: 0.0866\n",
            "Epoch 505/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0887 - val_loss: 0.0162 - val_mae: 0.1036\n",
            "Epoch 506/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0167 - val_mae: 0.1039\n",
            "Epoch 507/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0879 - val_loss: 0.0116 - val_mae: 0.0858\n",
            "Epoch 508/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0123 - mae: 0.0903 - val_loss: 0.0138 - val_mae: 0.0930\n",
            "Epoch 509/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0886 - val_loss: 0.0115 - val_mae: 0.0854\n",
            "Epoch 510/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0896 - val_loss: 0.0120 - val_mae: 0.0873\n",
            "Epoch 511/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0118 - mae: 0.0877 - val_loss: 0.0118 - val_mae: 0.0873\n",
            "Epoch 512/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0873 - val_loss: 0.0124 - val_mae: 0.0899\n",
            "Epoch 513/600\n",
            "38/38 [==============================] - 0s 7ms/step - loss: 0.0123 - mae: 0.0894 - val_loss: 0.0117 - val_mae: 0.0868\n",
            "Epoch 514/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0882 - val_loss: 0.0112 - val_mae: 0.0846\n",
            "Epoch 515/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0884 - val_loss: 0.0129 - val_mae: 0.0906\n",
            "Epoch 516/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0883 - val_loss: 0.0109 - val_mae: 0.0834\n",
            "Epoch 517/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0887 - val_loss: 0.0124 - val_mae: 0.0897\n",
            "Epoch 518/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0896 - val_loss: 0.0122 - val_mae: 0.0884\n",
            "Epoch 519/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0899 - val_loss: 0.0141 - val_mae: 0.0948\n",
            "Epoch 520/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0126 - mae: 0.0911 - val_loss: 0.0109 - val_mae: 0.0830\n",
            "Epoch 521/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0123 - val_mae: 0.0885\n",
            "Epoch 522/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0147 - val_mae: 0.0966\n",
            "Epoch 523/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0871 - val_loss: 0.0141 - val_mae: 0.0969\n",
            "Epoch 524/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0119 - val_mae: 0.0874\n",
            "Epoch 525/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0893 - val_loss: 0.0142 - val_mae: 0.0961\n",
            "Epoch 526/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0886 - val_loss: 0.0158 - val_mae: 0.1005\n",
            "Epoch 527/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0116 - mae: 0.0861 - val_loss: 0.0137 - val_mae: 0.0931\n",
            "Epoch 528/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0883 - val_loss: 0.0128 - val_mae: 0.0899\n",
            "Epoch 529/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0879 - val_loss: 0.0134 - val_mae: 0.0920\n",
            "Epoch 530/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0877 - val_loss: 0.0119 - val_mae: 0.0874\n",
            "Epoch 531/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0884 - val_loss: 0.0114 - val_mae: 0.0857\n",
            "Epoch 532/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0883 - val_loss: 0.0120 - val_mae: 0.0873\n",
            "Epoch 533/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0880 - val_loss: 0.0129 - val_mae: 0.0903\n",
            "Epoch 534/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0906 - val_loss: 0.0109 - val_mae: 0.0835\n",
            "Epoch 535/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0873 - val_loss: 0.0122 - val_mae: 0.0879\n",
            "Epoch 536/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.0861 - val_loss: 0.0109 - val_mae: 0.0828\n",
            "Epoch 537/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0870 - val_loss: 0.0113 - val_mae: 0.0854\n",
            "Epoch 538/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0872 - val_loss: 0.0112 - val_mae: 0.0847\n",
            "Epoch 539/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0898 - val_loss: 0.0115 - val_mae: 0.0863\n",
            "Epoch 540/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0124 - mae: 0.0901 - val_loss: 0.0117 - val_mae: 0.0866\n",
            "Epoch 541/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0891 - val_loss: 0.0146 - val_mae: 0.0980\n",
            "Epoch 542/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0881 - val_loss: 0.0108 - val_mae: 0.0826\n",
            "Epoch 543/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0903 - val_loss: 0.0107 - val_mae: 0.0823\n",
            "Epoch 544/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0893 - val_loss: 0.0111 - val_mae: 0.0841\n",
            "Epoch 545/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0125 - mae: 0.0899 - val_loss: 0.0116 - val_mae: 0.0861\n",
            "Epoch 546/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0883 - val_loss: 0.0108 - val_mae: 0.0834\n",
            "Epoch 547/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0117 - mae: 0.0869 - val_loss: 0.0114 - val_mae: 0.0855\n",
            "Epoch 548/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0116 - mae: 0.0862 - val_loss: 0.0108 - val_mae: 0.0828\n",
            "Epoch 549/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0890 - val_loss: 0.0115 - val_mae: 0.0849\n",
            "Epoch 550/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0118 - mae: 0.0866 - val_loss: 0.0124 - val_mae: 0.0894\n",
            "Epoch 551/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0115 - mae: 0.0863 - val_loss: 0.0123 - val_mae: 0.0900\n",
            "Epoch 552/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0881 - val_loss: 0.0124 - val_mae: 0.0881\n",
            "Epoch 553/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0135 - val_mae: 0.0922\n",
            "Epoch 554/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0883 - val_loss: 0.0154 - val_mae: 0.0981\n",
            "Epoch 555/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0878 - val_loss: 0.0119 - val_mae: 0.0870\n",
            "Epoch 556/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0124 - mae: 0.0895 - val_loss: 0.0125 - val_mae: 0.0897\n",
            "Epoch 557/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.0871 - val_loss: 0.0125 - val_mae: 0.0896\n",
            "Epoch 558/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0901 - val_loss: 0.0114 - val_mae: 0.0854\n",
            "Epoch 559/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.0873 - val_loss: 0.0112 - val_mae: 0.0845\n",
            "Epoch 560/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0888 - val_loss: 0.0131 - val_mae: 0.0903\n",
            "Epoch 561/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0123 - mae: 0.0885 - val_loss: 0.0112 - val_mae: 0.0841\n",
            "Epoch 562/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0119 - mae: 0.0881 - val_loss: 0.0112 - val_mae: 0.0846\n",
            "Epoch 563/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0120 - val_mae: 0.0885\n",
            "Epoch 564/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0117 - mae: 0.0872 - val_loss: 0.0108 - val_mae: 0.0832\n",
            "Epoch 565/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0891 - val_loss: 0.0127 - val_mae: 0.0904\n",
            "Epoch 566/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0887 - val_loss: 0.0120 - val_mae: 0.0881\n",
            "Epoch 567/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0123 - mae: 0.0889 - val_loss: 0.0130 - val_mae: 0.0910\n",
            "Epoch 568/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0878 - val_loss: 0.0140 - val_mae: 0.0968\n",
            "Epoch 569/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0877 - val_loss: 0.0120 - val_mae: 0.0876\n",
            "Epoch 570/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0122 - mae: 0.0895 - val_loss: 0.0124 - val_mae: 0.0899\n",
            "Epoch 571/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0876 - val_loss: 0.0146 - val_mae: 0.0963\n",
            "Epoch 572/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0118 - mae: 0.0879 - val_loss: 0.0136 - val_mae: 0.0946\n",
            "Epoch 573/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0908 - val_loss: 0.0170 - val_mae: 0.1045\n",
            "Epoch 574/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0120 - mae: 0.0893 - val_loss: 0.0114 - val_mae: 0.0855\n",
            "Epoch 575/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0115 - mae: 0.0864 - val_loss: 0.0147 - val_mae: 0.0981\n",
            "Epoch 576/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0885 - val_loss: 0.0124 - val_mae: 0.0886\n",
            "Epoch 577/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0885 - val_loss: 0.0136 - val_mae: 0.0929\n",
            "Epoch 578/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0121 - mae: 0.0884 - val_loss: 0.0121 - val_mae: 0.0878\n",
            "Epoch 579/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0891 - val_loss: 0.0120 - val_mae: 0.0876\n",
            "Epoch 580/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0895 - val_loss: 0.0111 - val_mae: 0.0842\n",
            "Epoch 581/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0877 - val_loss: 0.0135 - val_mae: 0.0941\n",
            "Epoch 582/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0126 - mae: 0.0899 - val_loss: 0.0112 - val_mae: 0.0845\n",
            "Epoch 583/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0119 - mae: 0.0883 - val_loss: 0.0121 - val_mae: 0.0887\n",
            "Epoch 584/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0120 - mae: 0.0892 - val_loss: 0.0120 - val_mae: 0.0877\n",
            "Epoch 585/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0123 - mae: 0.0906 - val_loss: 0.0155 - val_mae: 0.0990\n",
            "Epoch 586/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0890 - val_loss: 0.0121 - val_mae: 0.0876\n",
            "Epoch 587/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0121 - mae: 0.0889 - val_loss: 0.0120 - val_mae: 0.0876\n",
            "Epoch 588/600\n",
            "38/38 [==============================] - 0s 3ms/step - loss: 0.0122 - mae: 0.0896 - val_loss: 0.0122 - val_mae: 0.0886\n",
            "Epoch 589/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0122 - mae: 0.0889 - val_loss: 0.0165 - val_mae: 0.1045\n",
            "Epoch 590/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0118 - mae: 0.0879 - val_loss: 0.0143 - val_mae: 0.0950\n",
            "Epoch 591/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0892 - val_loss: 0.0136 - val_mae: 0.0941\n",
            "Epoch 592/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0121 - mae: 0.0889 - val_loss: 0.0122 - val_mae: 0.0886\n",
            "Epoch 593/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0122 - mae: 0.0876 - val_loss: 0.0119 - val_mae: 0.0870\n",
            "Epoch 594/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0889 - val_loss: 0.0119 - val_mae: 0.0880\n",
            "Epoch 595/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0119 - mae: 0.0877 - val_loss: 0.0114 - val_mae: 0.0851\n",
            "Epoch 596/600\n",
            "38/38 [==============================] - 0s 4ms/step - loss: 0.0118 - mae: 0.0875 - val_loss: 0.0115 - val_mae: 0.0857\n",
            "Epoch 597/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0120 - mae: 0.0880 - val_loss: 0.0118 - val_mae: 0.0878\n",
            "Epoch 598/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0117 - mae: 0.0875 - val_loss: 0.0121 - val_mae: 0.0880\n",
            "Epoch 599/600\n",
            "38/38 [==============================] - 0s 5ms/step - loss: 0.0121 - mae: 0.0893 - val_loss: 0.0116 - val_mae: 0.0855\n",
            "Epoch 600/600\n",
            "38/38 [==============================] - 0s 6ms/step - loss: 0.0121 - mae: 0.0885 - val_loss: 0.0111 - val_mae: 0.0843\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "绘制损失图"
      ],
      "metadata": {
        "id": "-Whp-tx3fgYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw a graph of the loss, which is the distance between\n",
        "# the predicted and actual values during training and validation.\n",
        "loss = history_2.history['loss']\n",
        "val_loss = history_2.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Mc1DlFO1U5gD",
        "outputId": "03a26904-9660-4f06-a5f6-db7b72118d99"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "跳过前100个轮次"
      ],
      "metadata": {
        "id": "YVgeRncqf44v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exclude the first few epochs so the graph is easier to read\n",
        "SKIP = 100\n",
        "plt.clf()\n",
        "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
        "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "qe_Az3mrf2Cx",
        "outputId": "3e026fd3-2a6b-4671-d0bc-0933cdc66aa7"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "对相同的轮次绘制平均绝对误差"
      ],
      "metadata": {
        "id": "LL4b4D2wgT_w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.clf()\n",
        "# Draw a graph of mean absolute error, which is another way of\n",
        "# measuring the amount of error in the prediction.\n",
        "mae = history_2.history['mae']\n",
        "val_mae = history_2.history['val_mae']\n",
        "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
        "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
        "plt.title('Training and validation mean absolute error')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "68qW4sOxgPDC",
        "outputId": "5ee6326d-0557-4e0e-b857-30613dd93d39"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "三、测试模型"
      ],
      "metadata": {
        "id": "adaclfTrg_eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate and print the loss on our test dataset\n",
        "loss = model_2.evaluate(x_test, y_test)\n",
        "# Make predictions based on our test dataset\n",
        "predictions = model_2.predict(x_test)\n",
        "# Graph the predictions against the actual values\n",
        "plt.clf()\n",
        "plt.title('Comparison of predictions and actual values')\n",
        "plt.plot(x_test, y_test, 'b.', label='Actual')\n",
        "plt.plot(x_test, predictions, 'r.', label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "wWwIkiseg8yD",
        "outputId": "84180c07-86aa-49fa-f8ea-37fe0b079c4e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.0090 - mae: 0.0736\n",
            "7/7 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "四、为TensorFlow Lite转换模型。TensorFlow Lite，这是一套用于在“边缘设备”（edge device）——从手机到微控制器——上运行TensorFlow模型的工具。"
      ],
      "metadata": {
        "id": "Q_GksqW2h6H-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1、我们使用转换器创建和保存模型的两个新版本。第一种转换为TensorFlow Lite FlatBuffer格式，但没有进行任何优化。第二个版本应用量化优化。"
      ],
      "metadata": {
        "id": "G59uVmnLjQXE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the model to the TensorFlow Lite format without quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n",
        "tflite_model = converter.convert()\n",
        "# Save the model to disk\n",
        "open(\"sine_model.tflite\",\"wb\").write(tflite_model)\n",
        "# Convert the model to the TensorFlow Lite format with quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n",
        "# Indicate that we want to perform the default optimizations,\n",
        "# which include quantization\n",
        "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
        "# Define a generator function that provides our test data's x values\n",
        "# as a representative dataset, and tell the converter to use it\n",
        "def representative_dataset_generator():\n",
        " for value in x_test:\n",
        "  # Each scalar value must be inside of a 2D array that is wrapped in a list\n",
        "  yield [np.array(value, dtype=np.float32, ndmin=2)]\n",
        "converter.representative_dataset = representative_dataset_generator\n",
        "# Convert the model\n",
        "tflite_model = converter.convert()\n",
        "# Save the model to disk\n",
        "open(\"sine_model_quantized.tflite\",\"wb\").write(tflite_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWdj9Rgch3M7",
        "outputId": "a3a2a5a8-a9a2-479e-aeb3-52fb15426151"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py:947: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3040"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "对两个模型进行预测，并将预测结果与原始未转化模型的结果一起绘制在图上"
      ],
      "metadata": {
        "id": "GMstCODhlLqc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate an interpreter for each model\n",
        "sine_model = tf.lite.Interpreter('sine_model.tflite')\n",
        "sine_model_quantized = tf.lite.Interpreter('sine_model_quantized.tflite')\n",
        "\n",
        "# Allocate memory for each model\n",
        "sine_model.allocate_tensors()\n",
        "sine_model_quantized.allocate_tensors()\n",
        "\n",
        "# Get indexes of the input and output tensors\n",
        "sine_model_input_index = sine_model.get_input_details()[0][\"index\"]\n",
        "sine_model_output_index = sine_model.get_output_details()[0][\"index\"]\n",
        "sine_model_quantized_input_index = sine_model_quantized.get_input_details()[0]\n",
        "[\"index\"]\n",
        "sine_model_quantized_output_index = \\\n",
        " sine_model_quantized.get_output_details()[0][\"index\"]\n",
        "\n",
        "# Create arrays to store the results\n",
        "sine_model_predictions = []\n",
        "sine_model_quantized_predictions = []\n",
        "\n",
        "# Run each model's interpreter for each value and store the results in arrays\n",
        "for x_value in x_test:\n",
        " # Create a 2D tensor wrapping the current x value\n",
        " x_value_tensor = tf.convert_to_tensor([[x_value]], dtype=np.float32)\n",
        " # Write the value to the input tensor\n",
        " sine_model.set_tensor(sine_model_input_index, x_value_tensor)\n",
        " # Run inference\n",
        " sine_model.invoke()\n",
        " # Read the prediction from the output tensor\n",
        " sine_model_predictions.append(\n",
        "  sine_model.get_tensor(sine_model_output_index)[0])\n",
        " # Do the same for the quantized model\n",
        " sine_model_quantized.set_tensor(sine_model_quantized_input_index[\"index\"], x_value_tensor)\n",
        " sine_model_quantized.invoke()\n",
        " sine_model_quantized_predictions.append(\n",
        "  sine_model_quantized.get_tensor(sine_model_quantized_output_index)[0])\n",
        "# See how they line up with the data\n",
        "plt.clf()\n",
        "plt.title('Comparison of various models against actual values')\n",
        "plt.plot(x_test, y_test, 'bo', label='Actual')\n",
        "plt.plot(x_test, predictions, 'ro', label='Original predictions')\n",
        "plt.plot(x_test, sine_model_predictions, 'bx', label='Lite predictions')\n",
        "plt.plot(x_test, sine_model_quantized_predictions, 'gx', \\\n",
        " label='Lite quantized predictions')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "Am5YUKT7jtav",
        "outputId": "579f0011-49fa-43e1-9ec3-d8a69470234d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "计算转换后模型的大小并进行比较"
      ],
      "metadata": {
        "id": "xHjCqa1dvxqq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "basic_model_size = os.path.getsize(\"sine_model.tflite\")\n",
        "print(\"Basic model is %d bytes\" % basic_model_size)\n",
        "quantized_model_size = os.path.getsize(\"sine_model_quantized.tflite\")\n",
        "print(\"Quantized model is %d bytes\" % quantized_model_size)\n",
        "difference = basic_model_size - quantized_model_size\n",
        "print(\"Difference is %d bytes\" % difference)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jE7w3QF2v3Y4",
        "outputId": "7a6bb69a-3c9f-4237-a4ca-1dad965efcaa"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Basic model is 3192 bytes\n",
            "Quantized model is 3040 bytes\n",
            "Difference is 152 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "以下单元格在我们的量化模型上运行xxd，将输出写入名为sine_model_quantized.cc的文件中，并将其打印到屏幕上："
      ],
      "metadata": {
        "id": "10UySU_7wWxJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install xxd if it is not available\n",
        "!apt-get -qq install xxd\n",
        "# Save the file as a C source file\n",
        "!xxd -i sine_model_quantized.tflite > sine_model_quantized.cc\n",
        "# Print the source file\n",
        "!cat sine_model_quantized.cc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgWLKEkUw1FP",
        "outputId": "1b0b29cb-be3a-444b-9ec1-d5a5a1161bfa"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unsigned char sine_model_quantized_tflite[] = {\n",
            "  0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00,\n",
            "  0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,\n",
            "  0x88, 0x00, 0x00, 0x00, 0xe0, 0x00, 0x00, 0x00, 0xac, 0x03, 0x00, 0x00,\n",
            "  0xbc, 0x03, 0x00, 0x00, 0x58, 0x0b, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0xfc, 0xff, 0xff,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00,\n",
            "  0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f,\n",
            "  0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x94, 0xff, 0xff, 0xff, 0x0b, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73,\n",
            "  0x65, 0x5f, 0x34, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0xd6, 0xfc, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00,\n",
            "  0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e, 0x70, 0x75,\n",
            "  0x74, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0xdc, 0xff, 0xff, 0xff, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x43, 0x4f, 0x4e, 0x56,\n",
            "  0x45, 0x52, 0x53, 0x49, 0x4f, 0x4e, 0x5f, 0x4d, 0x45, 0x54, 0x41, 0x44,\n",
            "  0x41, 0x54, 0x41, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e, 0x5f, 0x72, 0x75, 0x6e, 0x74,\n",
            "  0x69, 0x6d, 0x65, 0x5f, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00,\n",
            "  0x0f, 0x00, 0x00, 0x00, 0xc8, 0x02, 0x00, 0x00, 0xc0, 0x02, 0x00, 0x00,\n",
            "  0xac, 0x02, 0x00, 0x00, 0x84, 0x02, 0x00, 0x00, 0x34, 0x02, 0x00, 0x00,\n",
            "  0x24, 0x01, 0x00, 0x00, 0xd4, 0x00, 0x00, 0x00, 0xb4, 0x00, 0x00, 0x00,\n",
            "  0xac, 0x00, 0x00, 0x00, 0xa4, 0x00, 0x00, 0x00, 0x9c, 0x00, 0x00, 0x00,\n",
            "  0x94, 0x00, 0x00, 0x00, 0x8c, 0x00, 0x00, 0x00, 0x6c, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x8e, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x58, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0e, 0x00,\n",
            "  0x08, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0xeb, 0x03, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x10, 0x00, 0x0c, 0x00,\n",
            "  0x08, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,\n",
            "  0x32, 0x2e, 0x31, 0x34, 0x2e, 0x30, 0x00, 0x00, 0xf2, 0xfd, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x31, 0x2e, 0x31, 0x34,\n",
            "  0x2e, 0x30, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x74, 0xf6, 0xff, 0xff, 0x78, 0xf6, 0xff, 0xff, 0x7c, 0xf6, 0xff, 0xff,\n",
            "  0x80, 0xf6, 0xff, 0xff, 0x84, 0xf6, 0xff, 0xff, 0x22, 0xfe, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x93, 0x3d, 0x2f, 0xc8,\n",
            "  0x95, 0xd2, 0x81, 0x53, 0x45, 0x2d, 0x01, 0x8b, 0x65, 0xd4, 0x6a, 0xf2,\n",
            "  0x3e, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0xf8, 0xff, 0xff, 0xff, 0xdb, 0x18, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x57, 0xe5, 0xff, 0xff, 0x51, 0xe5, 0xff, 0xff,\n",
            "  0x04, 0x26, 0x00, 0x00, 0x67, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x58, 0xe7, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0xe8, 0xff, 0xff, 0xff,\n",
            "  0xd5, 0x20, 0x00, 0x00, 0x8a, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x01, 0x00, 0x00, 0x10, 0xe1, 0x12, 0x16, 0x11, 0x16, 0x0f, 0xe3,\n",
            "  0xff, 0xf3, 0xda, 0xf5, 0x11, 0x08, 0xf4, 0x02, 0xde, 0xe9, 0xe2, 0x1d,\n",
            "  0xea, 0xf4, 0xea, 0x20, 0x11, 0x0d, 0xe8, 0x25, 0xdd, 0x23, 0xde, 0x10,\n",
            "  0x0a, 0xf8, 0x22, 0xeb, 0xe5, 0x00, 0xf7, 0xc2, 0xc6, 0x2d, 0x0e, 0xf1,\n",
            "  0xc2, 0x0b, 0xfe, 0x46, 0xff, 0x07, 0xf2, 0xf6, 0x1c, 0xe9, 0xfc, 0xd7,\n",
            "  0xe3, 0x1f, 0xf8, 0x24, 0x01, 0xef, 0xff, 0x23, 0x1b, 0xac, 0xee, 0xea,\n",
            "  0x01, 0xe4, 0x20, 0xc9, 0xeb, 0x16, 0x25, 0xdb, 0xef, 0xf8, 0x81, 0x2f,\n",
            "  0xed, 0xea, 0x0f, 0x06, 0xe8, 0xf9, 0x1c, 0xe6, 0xdd, 0x1c, 0xf4, 0xf1,\n",
            "  0xfa, 0xf3, 0xe5, 0x59, 0x15, 0xeb, 0xe8, 0x13, 0xe9, 0x24, 0xf2, 0x1a,\n",
            "  0xe9, 0xdd, 0xe8, 0x08, 0x1d, 0xf4, 0xe2, 0x1a, 0xfd, 0x0a, 0x03, 0xdd,\n",
            "  0x06, 0xe2, 0xe4, 0xdd, 0x13, 0xe1, 0xde, 0xe3, 0x19, 0xfd, 0xfa, 0xfb,\n",
            "  0xe9, 0xfa, 0xf7, 0x00, 0x1c, 0xdd, 0x1f, 0xeb, 0x0b, 0x02, 0x07, 0xf7,\n",
            "  0x09, 0x17, 0xdc, 0xea, 0x11, 0xe3, 0xda, 0x0c, 0xe6, 0x0e, 0xed, 0x10,\n",
            "  0xff, 0xf7, 0xec, 0xfa, 0x0c, 0x1b, 0xf6, 0xff, 0xf1, 0x04, 0x28, 0x1f,\n",
            "  0xec, 0xf9, 0x11, 0xe7, 0xed, 0xfd, 0xec, 0x25, 0xe4, 0x25, 0x06, 0x0b,\n",
            "  0x01, 0xfc, 0x19, 0xe9, 0xe7, 0xde, 0x09, 0xdd, 0xda, 0xeb, 0xfb, 0x23,\n",
            "  0x0a, 0x0d, 0x05, 0x00, 0x22, 0xea, 0x19, 0x19, 0xf1, 0xf3, 0xf0, 0xf4,\n",
            "  0xbe, 0x0a, 0xdc, 0xed, 0xd6, 0x06, 0x21, 0x24, 0x0d, 0xf1, 0xdb, 0xde,\n",
            "  0x20, 0x16, 0x08, 0x07, 0x22, 0x01, 0x1d, 0x1e, 0xfa, 0xf2, 0xf9, 0x1b,\n",
            "  0xdb, 0x10, 0x20, 0xde, 0x00, 0xe9, 0x20, 0xbe, 0xa2, 0x11, 0x0a, 0x25,\n",
            "  0xcf, 0xda, 0xfe, 0x2f, 0xe4, 0xec, 0xe1, 0x17, 0x11, 0x0a, 0x0c, 0xe3,\n",
            "  0xde, 0x1d, 0xe4, 0x13, 0x05, 0x25, 0xe2, 0xfc, 0x96, 0xff, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x5e, 0xff, 0xff, 0xff,\n",
            "  0x6d, 0xfd, 0xff, 0xff, 0x85, 0x0d, 0x00, 0x00, 0xe9, 0x07, 0x00, 0x00,\n",
            "  0x48, 0x08, 0x00, 0x00, 0x9e, 0x17, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc4, 0xff, 0xff, 0xff,\n",
            "  0xc3, 0x05, 0x00, 0x00, 0xe8, 0xfe, 0xff, 0xff, 0xca, 0x0b, 0x00, 0x00,\n",
            "  0xb0, 0xff, 0xff, 0xff, 0xc9, 0x0c, 0x00, 0x00, 0xd3, 0xfd, 0xff, 0xff,\n",
            "  0xe2, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x0b, 0x2e, 0x69, 0xd9, 0xc7, 0xa5, 0x09, 0xf3, 0xd9, 0xe5, 0xed, 0xd1,\n",
            "  0xb9, 0xe7, 0x7f, 0x1e, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x1d, 0xfd, 0xff, 0xff, 0x7c, 0xf8, 0xff, 0xff, 0x80, 0xf8, 0xff, 0xff,\n",
            "  0x0f, 0x00, 0x00, 0x00, 0x4d, 0x4c, 0x49, 0x52, 0x20, 0x43, 0x6f, 0x6e,\n",
            "  0x76, 0x65, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x14, 0x00,\n",
            "  0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x40, 0x01, 0x00, 0x00,\n",
            "  0x44, 0x01, 0x00, 0x00, 0x48, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x01, 0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0x6c, 0x00, 0x00, 0x00,\n",
            "  0x34, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x00,\n",
            "  0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x92, 0xff, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x08, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x30, 0xf9, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xc6, 0xff, 0xff, 0xff,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xb6, 0xff, 0xff, 0xff,\n",
            "  0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,\n",
            "  0x03, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x1a, 0x00, 0x14, 0x00,\n",
            "  0x10, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x1c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x1c, 0x00, 0x00, 0x00,\n",
            "  0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00,\n",
            "  0x08, 0x00, 0x07, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,\n",
            "  0x07, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x0a, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0xc8, 0x05, 0x00, 0x00, 0x28, 0x05, 0x00, 0x00, 0xa0, 0x04, 0x00, 0x00,\n",
            "  0x24, 0x04, 0x00, 0x00, 0xb8, 0x03, 0x00, 0x00, 0x3c, 0x03, 0x00, 0x00,\n",
            "  0xd0, 0x02, 0x00, 0x00, 0x5c, 0x02, 0x00, 0x00, 0x90, 0x01, 0x00, 0x00,\n",
            "  0xdc, 0x00, 0x00, 0x00, 0x60, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x7e, 0xfa, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x1c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x34, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x68, 0xfa, 0xff, 0xff, 0x19, 0x00, 0x00, 0x00,\n",
            "  0x53, 0x74, 0x61, 0x74, 0x65, 0x66, 0x75, 0x6c, 0x50, 0x61, 0x72, 0x74,\n",
            "  0x69, 0x74, 0x69, 0x6f, 0x6e, 0x65, 0x64, 0x43, 0x61, 0x6c, 0x6c, 0x3a,\n",
            "  0x30, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x26, 0xfe, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00,\n",
            "  0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x50, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x6c, 0xfb, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0xfd, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x45, 0xb3, 0xfe, 0x3b, 0x1a, 0x00, 0x00, 0x00,\n",
            "  0x53, 0x74, 0x61, 0x74, 0x65, 0x66, 0x75, 0x6c, 0x50, 0x61, 0x72, 0x74,\n",
            "  0x69, 0x74, 0x69, 0x6f, 0x6e, 0x65, 0x64, 0x43, 0x61, 0x6c, 0x6c, 0x3a,\n",
            "  0x30, 0x31, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x9e, 0xfe, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x88, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,\n",
            "  0xe4, 0xfb, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x36, 0xb6, 0x01, 0x3c, 0x52, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x3b, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33,\n",
            "  0x2f, 0x52, 0x65, 0x6c, 0x75, 0x3b, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e,\n",
            "  0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65,\n",
            "  0x5f, 0x33, 0x2f, 0x42, 0x69, 0x61, 0x73, 0x41, 0x64, 0x64, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x4e, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01, 0x18, 0x00, 0x00, 0x00,\n",
            "  0x20, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x09, 0x88, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0xff, 0xff, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0x94, 0xfc, 0xff, 0xff,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x54, 0x92, 0x33, 0x3c, 0x52, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,\n",
            "  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e,\n",
            "  0x73, 0x65, 0x5f, 0x32, 0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x3b,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x2f, 0x52, 0x65, 0x6c,\n",
            "  0x75, 0x3b, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c,\n",
            "  0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x2f, 0x42,\n",
            "  0x69, 0x61, 0x73, 0x41, 0x64, 0x64, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00,\n",
            "  0x20, 0x00, 0x1c, 0x00, 0x1b, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x07, 0x00, 0x16, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x01, 0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,\n",
            "  0x40, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,\n",
            "  0x48, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x5c, 0xfd, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff,\n",
            "  0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0xc9, 0x80, 0xc9, 0x3c, 0x0c, 0x00, 0x00, 0x00, 0x74, 0x66, 0x6c, 0x2e,\n",
            "  0x71, 0x75, 0x61, 0x6e, 0x74, 0x69, 0x7a, 0x65, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0xd2, 0xfd, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x30, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,\n",
            "  0x44, 0x00, 0x00, 0x00, 0xbc, 0xfd, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x7a, 0xe2, 0x89, 0x3b,\n",
            "  0x1b, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32,\n",
            "  0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x3a, 0xfe, 0xff, 0xff,\n",
            "  0x00, 0x00, 0x00, 0x01, 0x14, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x58, 0x00, 0x00, 0x00,\n",
            "  0x24, 0xfe, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x5f, 0x10, 0xd9, 0x38,\n",
            "  0x2b, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32,\n",
            "  0x2f, 0x42, 0x69, 0x61, 0x73, 0x41, 0x64, 0x64, 0x2f, 0x52, 0x65, 0x61,\n",
            "  0x64, 0x56, 0x61, 0x72, 0x69, 0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xb2, 0xfe, 0xff, 0xff,\n",
            "  0x00, 0x00, 0x00, 0x01, 0x14, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00,\n",
            "  0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x44, 0x00, 0x00, 0x00,\n",
            "  0x9c, 0xfe, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x73, 0x2d, 0x37, 0x3c, 0x1b, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x1a, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x02, 0x58, 0x00, 0x00, 0x00, 0x04, 0xff, 0xff, 0xff,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x7b, 0x7d, 0x00, 0x39, 0x2b, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33, 0x2f, 0x42, 0x69, 0x61,\n",
            "  0x73, 0x41, 0x64, 0x64, 0x2f, 0x52, 0x65, 0x61, 0x64, 0x56, 0x61, 0x72,\n",
            "  0x69, 0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x92, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x09, 0x48, 0x00, 0x00, 0x00, 0x7c, 0xff, 0xff, 0xff,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x7b, 0x42, 0x36, 0x3c, 0x1b, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00, 0x1c, 0x00, 0x18, 0x00,\n",
            "  0x17, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x07, 0x00, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x20, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x02, 0x64, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x0c, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x73, 0xb2, 0xb8, 0x38, 0x2b, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x2f, 0x42, 0x69, 0x61,\n",
            "  0x73, 0x41, 0x64, 0x64, 0x2f, 0x52, 0x65, 0x61, 0x64, 0x56, 0x61, 0x72,\n",
            "  0x69, 0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00, 0x1c, 0x00, 0x18, 0x00,\n",
            "  0x00, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x07, 0x00, 0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76,\n",
            "  0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f,\n",
            "  0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e, 0x70, 0x75,\n",
            "  0x74, 0x3a, 0x30, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00,\n",
            "  0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xf0, 0xff, 0xff, 0xff,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06,\n",
            "  0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x09, 0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x72, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x72\n",
            "};\n",
            "unsigned int sine_model_quantized_tflite_len = 3040;\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "szdNyo1WxFBr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}